Dissertations / Theses on the topic 'Sensor fusion'

Many machines in these days have sensors to collect information from the world they inhabit. The correctness of this information is crucial for the correct operation. However, at times sensors are not so reliable since they are sometimes affected of some type of noise and thus give incorrect information. Another drawback might be lack of information due to shortage of existing sensors. Sensor fusion is trying to overcome these drawbacks by integrating or combining information from multiple sensors. The heating of a building is a slow and time consuming process, i.e. either the flow or energy consumption are object to drastically changes. On the other hand, the tap water system, i.e. the heating of tap water can be the source to severe changes in both flow and energy consumption. This because of that the flow is stochastic in the tap water system, at any given time a tap may be opened or closed and therefore drastically change the flow. The purpose of this thesis is to investigate if is it possible to use sensor fusion to get accurate continuous flow values from a district heating substation. This is done by integrating different sensor fusion algorithms in a district heating substation simulator.

Pre-feasibility analysis on the optimization of the performance of the indirect tyre pressure monitoring system through a sensor fusion with a new generation of ultrasound parking sensors: from the idea to the development of macro project specifications and macro business case, with definition of the possible new scenario in terms of performance, costs and perceived quality

Mapping stationary objects and tracking moving targets are essential for many autonomous functions in vehicles. In order to compute the map and track estimates, sensor measurements from radar, laser and camera are used together with the standard proprioceptive sensors present in a car. By fusing information from different types of sensors, the accuracy and robustness of the estimates can be increased. Different types of maps are discussed and compared in the thesis. In particular, road maps make use of the fact that roads are highly structured, which allows relatively simple and powerful models to be employed. It is shown how the information of the lane markings, obtained by a front looking camera, can be fused with inertial measurement of the vehicle motion and radar measurements of vehicles ahead to compute a more accurate and robust road geometry estimate. Further, it is shown how radar measurements of stationary targets can be used to estimate the road edges, modeled as polynomials and tracked as extended targets. Recent advances in the field of multiple target tracking lead to the use of finite set statistics (FISST) in a set theoretic approach, where the targets and the measurements are treated as random finite sets (RFS). The first order moment of a RFS is called probability hypothesis density (PHD), and it is propagated in time with a PHD filter. In this thesis, the PHD filter is applied to radar data for constructing a parsimonious representation of the map of the stationary objects around the vehicle. Two original contributions, which exploit the inherent structure in the map, are proposed. A data clustering algorithm is suggested to structure the description of the prior and considerably improving the update in the PHD filter. Improvements in the merging step further simplify the map representation. When it comes to tracking moving targets, the focus of this thesis is on extended targets, i.e., targets which potentially may give rise to more than one measurement per time step. An implementation of the PHD filter, which was proposed to handle data obtained from extended targets, is presented. An approximation is proposed in order to limit the number of hypotheses. Further, a framework to track the size and shape of a target is introduced. The method is based on measurement generating points on the surface of the target, which are modeled by an RFS. Finally, an efficient and novel Bayesian method is proposed for approximating the tire radii of a vehicle based on particle filters and the marginalization concept. This is done under the assumption that a change in the tire radius is caused by a change in tire pressure, thus obtaining an indirect tire pressure monitoring system. The approaches presented in this thesis have all been evaluated on real data from both freeways and rural roads in Sweden.
SEFS -- IVSS
VR - ETT

This thesis presents a new approach to multi-rate sensor fusion for (1) user matching and (2) position stabilisation and lag reduction. The Microsoft Kinect sensor and the inertial sensors in a mobile device are fused with a Gaussian Process (GP) prior method. We present a Gaussian Process prior model-based framework for multisensor data fusion and explore the use of this model for fusing mobile inertial sensors and an external position sensing device. The Gaussian Process prior model provides a principled mechanism for incorporating the low-sampling-rate position measurements and the high-sampling-rate derivatives in multi-rate sensor fusion, which takes account of the uncertainty of each sensor type. We explore the complementary properties of the Kinect sensor and the built-in inertial sensors in a mobile device and apply the GP framework for sensor fusion in the mobile human-computer interaction area. The Gaussian Process prior model-based sensor fusion is presented as a principled probabilistic approach to dealing with position uncertainty and the lag of the system, which are critical for indoor augmented reality (AR) and other location-aware sensing applications. The sensor fusion helps increase the stability of the position and reduce the lag. This is of great benefit for improving the usability of a human-computer interaction system. We develop two applications using the novel and improved GP prior model. (1) User matching and identification. We apply the GP model to identify individual users, by matching the observed Kinect skeletons with the sensed inertial data from their mobile devices. (2) Position stabilisation and lag reduction in a spatially aware display application for user performance improvement. We conduct a user study. Experimental results show the improved accuracy of target selection, and reduced delay from the sensor fusion system, allowing the users to acquire the target more rapidly, and with fewer errors in comparison with the Kinect filtered system. They also reported improved performance in subjective questions. The two applications can be combined seamlessly in a proxemic interaction system as identification of people and their positions in a room-sized environment plays a key role in proxemic interactions.

Mestrado em Engenharia Electrónica e Telecomunicações
A fus~ao sensorial combina pe cas de informa c~ao proveniente de diferentes fontes/sensores de modo a obter informa c~ao global mais precisa quando comparada com sistemas que apenas dependem de fontes/sensores. Diferentes m etodos de fus~ao sensorial t^em sido desenvolvidos de forma a optimizar a resposta geral dos sistemas. Resultados nais, como a unidade inercial que funde duas fam lias diferentes de sensores para dar uma estimativa mais precisa/melhor dos dados sensoriais ou a auto-localiza c~ao do robot que deve ser capaz de avaliar a sua pr opria posi c~ao e consequentemente a posi c~ao dos membros da sua equipa s~ao exemplos da fus~ao sensorial. Esta tese ir a descrever detalhadamente, desde a fase de algoritmo at e a implementa c~ao juntamente com algumas bases matem aticas necess arias para a compreens~ao dos conceitos introduzidos, todo o trabalho desenvolvido para a equipa portuguesa que serviu para tornar o objectivo proposto em realidade: participar pela primeira vez na categoria Standard Platform League no RoboCup 2012.
The technology of sensor fusion combines pieces of information coming from di erent sources/sensors, resulting in an enhanced overall information accuracy when compared with systems that rely only on sources/sensors. Di erent sensor fusion methods have been developed in order to optimize the overall system output. End results like the inertial unit that fuses two di erent sensor families to give a more accurate/better estimate of the sensory data or the self-localization of the robot that should be able to evaluate its position and consequently its team members position. A walk-through, from the algorithm phase to the implementation, will be given in this thesis along with some mathematical background necessary to comprehend the concepts introduced and description of the auxiliary tools that were built for the Portuguese Team to help accomplish the objective: First presence in the Standard Platform League in the RoboCup 2012.

Robust localization is a prerequisite for mobile robot autonomy. In many situations the GPS signal is not available and thus an additional localization system is required. A simple approach is to apply localization based on dead reckoning by use of wheel encoders but it results in large estimation errors. With exteroceptive sensors such as a laser range finder natural landmarks in the environment of the robot can be extracted from raw range data. Landmarks are extracted with the Hough transform and a recursive line segment algorithm. By applying data association and Kalman filtering along with process models the landmarks can be used in combination with wheel encoders for estimating the global position of the robot. If several robots can cooperate better position estimates are to be expected because robots can be seen as mobile landmarks and one robot can supervise the movement of another. The centralized Kalman filter presented in this master thesis systematically treats robots and extracted landmarks such that benefits from several robots are utilized. Experiments in different indoor environments with two different robots show that long distances can be traveled while the positional uncertainty is kept low. The benefit from cooperating robots in the sense of reduced positional uncertainty is also shown in an experiment.

Except for localization algorithms a typical autonomous robot task in the form of change detection is solved. The change detection method, which requires robust localization, is aimed to be used for surveillance. The implemented algorithm accounts for measurement- and positional uncertainty when determining whether something in the environment has changed. Consecutive true changes as well as sporadic false changes are detected in an illustrative experiment.

The use of radar and camera for situation awareness is gaining popularity in automotivesafety applications. In this thesis situation awareness consists of accurate estimates of theego vehicle’s motion, the position of the other vehicles and the road geometry. By fusinginformation from different types of sensors, such as radar, camera and inertial sensor, theaccuracy and robustness of those estimates can be increased.

Sensor fusion is the process of using information from several different sensors tocompute an estimate of the state of a dynamic system, that in some sense is better thanit would be if the sensors were used individually. Furthermore, the resulting estimate isin some cases only obtainable through the use of data from different types of sensors. Asystematic approach to handle sensor fusion problems is provided by model based stateestimation theory. The systems discussed in this thesis are primarily dynamic and they aremodeled using state space models. A measurement model is used to describe the relationbetween the state variables and the measurements from the different sensors. Within thestate estimation framework a process model is used to describe how the state variablespropagate in time. These two models are of major importance for the resulting stateestimate and are therefore given much attention in this thesis. One example of a processmodel is the single track vehicle model, which is used to model the ego vehicle’s motion.In this thesis it is shown how the estimate of the road geometry obtained directly from thecamera information can be improved by fusing it with the estimates of the other vehicles’positions on the road and the estimate of the radius of the ego vehicle’s currently drivenpath.

The positions of stationary objects, such as guardrails, lampposts and delineators aremeasured by the radar. These measurements can be used to estimate the border of theroad. Three conceptually different methods to represent and derive the road borders arepresented in this thesis. Occupancy grid mapping discretizes the map surrounding theego vehicle and the probability of occupancy is estimated for each grid cell. The secondmethod applies a constrained quadratic program in order to estimate the road borders,which are represented by two polynomials. The third method associates the radar measurementsto extended stationary objects and tracks them as extended targets.

The approaches presented in this thesis have all been evaluated on real data from bothfreeways and rural roads in Sweden.

IVSS - SEFS

In recent years researchers has focused on the development of techniques for multi-sensor data fusion systems. Data fusion systems process data from multiple sensors to develop improved estimate of the position, velocity, attributes and identity of entities such as the targets or entities of interest. Visualizing sensor data from fused data to raw data from each sensor help analysts to interpret the data and assess sensor data fusion platform, an evolving situation or threats. Immersive visualization has emerged as an ideal solution for exploration of sensor data and provides opportunities for improvement in multi sensor data fusion. The thesis aims to investigate possibilities of applying information visualization to sensor data fusion platform in Volvo. A visualization prototype is also developed to enables multiple users to interactively visualize Sensor Data Fusion platform in real-time, mainly in order to demonstrates, evaluate and analyze the platform functionality. In this industrial study two research methodologies were used; a case study and an experiment for evaluating the results. First a case study was conducted in order to find the best visualization technique for visualizing sensor data fusion platform. Second an experiment was conducted to evaluate the usability of the prototype that has been developed and make sure the user requirement were met. The visualization tool enabled us to study the effectiveness and efficiency of the visualization techniques used. The results confirm that the visualization method used is effective, efficient for visualizing sensor data fusion platform.

This thesis addresses the problem of design and analysis of distributed in-network signal processing algorithms for effcient aggregation and fusion of information in wireless sensor networks. The distributed in-network signal processing algorithms alleviate a number of drawbacks of the centralized fusion approach. The single point of failure, complex routing protocols, uneven power consumption in sensor nodes, ineffcient wireless channel utilization, and poor scalability are among these drawbacks. These drawbacks of the centralized approach lead to reduced network lifetime, poor robustness to node failures, and reduced network capacity. The distributed algorithms alleviate these issues by using simple pairwise message exchange protocols and localized in-network processing. However, for such algorithms accuracy losses and/or time required to complete a particular fusion task may be significant. The design and analysis of fast and accurate distributed algorithms with guaranteed performance characteristics is thus important. In this thesis two specific problems associated with the analysis and design of such distributed algorithms are addressed.
For the distributed average consensus algorithm a memory based acceleration methodology is proposed. The convergence of the proposed methodology is investigated. For the two important settings of this methodology, optimal values of system parameters are determined and improvement with respect to the standard distributed average consensus algorithm is theoretically characterized. The theoretical improvement characterization matches well with the results of numerical experiments revealing significant and well scaling gain. The practical distributed on-line initialization scheme is devised. Numerical experiments reveal the feasibility of the proposed initialization scheme and superior performance of the proposed methodology with respect to several existing acceleration approaches.
For the collaborative signal and information processing methodology a number of theoretical performance guarantees is obtained. The collaborative signal and information processing framework consists in activating only a cluster of wireless sensors to perform target tracking task in the cluster head using particle filter. The optimal cluster is determined at every time instant and cluster head hand-off is performed if necessary. To reduce communication costs only an approximation of the filtering distribution is sent during hand-off resulting in additional approximation errors. The time uniform performance guarantees accounting for the additional errors are obtained in two settings: the subsample approximation and the parametric mixture approximation hand-off.
Cette thèse aborde le problème de la conception et l'analyse d'algorithmes distribuès servant à l'agrégation efficace et la fusion de l'information dans des reséaux capteurs sans fil. Ces algorithmes distribuès servent à addresser un bon nombre d'inconvénients qu'ont les approches de fusion centralisée telles que le point de défaillance unique, les protocoles de routage complexe, la consommation de puissance inégale dans les noeuds de capteurs, l'utilisation inefficace des voies de transmission sans-fil et l'extensibilité limitée. Ces inconvénients de l'approche centralisée ont comme effet de réduire la durée de vie du reséau, la robustesse des noeuds face aux défaillances et la capacité du réseau. Les algorithmes distribuès atténuent ces problèmes en utilisant des simples protocoles de messageries entre les noeuds ainsi que du traitement d'information localisé. Toutefois, pour ces algorithmes, les pertes de précision et/ou de temps nécessaire pour effectuer une tâche peuvent être importantes. C'est pourquoi la conception et l'analyse d'algorithmes distribuès rapide et précis est importante. Dans cette thèse, deux problèmes spécifiques associés à l'analyse et le conception de tels algorithms sont abordés.
En ce qui concerne l'algorithme de consensus sur la moyenne distribuè, une méthode d'accélération fondé sur la mémoire est proposée et sa convergence analysée. Pour les deux paramètres importants de cette méthodologie, les valeurs optimales pour le système sont déterminées et l'amélioration par rapport à l'algorithme de consensus de base est caractérisée de façon théorique. Cette caractérisation correspond aux resultants d'expériences numériques et révèlent des gains importants et extensibles. Le régime distribuè d'initialisation en ligne est conçu. Des expériences numériques révèlevent la faisabilité du régime d'initilisation proposé ainsi qu'un rendement supérieur à plusieurs approches existantes.
Pour la méthodologie de traitement de signaux et d'information collaborative, un certain nombre de garanties théoriques de performance sont obtenues. Ce cadre de travail consiste à activer seulement une grappe de capteurs sans fil pour effectuer les tâches de pistage d'objet au niveau deu chef de groupe en utilisant un filtre particulaire. La grappe optimale est déterminée à chaque intervale de temps et le transfert du titre de chef de groupe est réalisé au besoin. Pour réduire les coûts de communication, seulement une approximation de la distribution du filtre est envoyé pendant le transfert de responsabilités ce qui entraîne des erreurs supplémentaires. Les garanties de performance uniformes dans le temps tenant compte de ces erreurs supplémentaires sont obtenues dans deux contextes.

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control.

Location estimation performance is not always satisfactory and improving it can be expensive. The performance of location estimation technology can be increased by refining the existing location estimation technologies. A better way of increasing performance is to use multiple technologies and combine the available data provided by them in order to obtain better results. Also, maintaining one's location privacy while using location estimation technology is a challenge. How can this problem be solved? In order to make it easier to perform sensor fusion on the available data and to speed up development, a flexible framework centered around a component-based architecture was designed. In order to test the performance of location estimation using the proposed sensor fusion framework, the framework and all the necessary components were implemented and tested. In order to solve the location estimation privacy issues, a comprehensive design that considers all aspects of the problem, from the physical aspects of using radio transmissions to communicating and using location data, is proposed. The experimental results of testing the location estimation sensor fusion framework show that by using sensor fusion, the availability of location estimation is always increased and the accuracy is always increased on average. The experimental results also allow the profiling of the sensor fusion framework's time and energy consumption. In the case of time consumption, there is a 0.32% - 17.06% - 5.05% - 77.58% split between results overhead, engine overhead, component communication time and component execution time on an average. The more measurements are gathered by the data gathering components, the more the component execution time increases relative to all the other execution times because component execution time is the only one that increases while the others remain constant.

Thanks to faster and better integrated processing units, as well as increasingly miniaturized and precise sensors, Robotics is experiencing a period of significant development. New technology is making robots increasingly more autonomous. Data from different sensors can be fused and processed in real time by on-board processing units in order to make the robot perform either more or less complicated tasks. Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAV) equipped with sensors and microprocessors/microcontrollers can be remotely piloted. They can also move autonomously with the help of different navigation and localization methods. Power electronics is contributing to a transformation in many fields such as robotics, automotive and consumer devices. Some new applications of power electronics are discussed in the first chapter of this thesis. One of these describes how new power electronics devices allow the use of distributed instead of centralized control in industrial robotics. Multi-sensor data fusion theory is presented in the second chapter; the Kalman Filter and Particle Filter are described. Inertial sensors and magnetometers are introduced in the third chapter with the description of a calibration procedure for each sensor. Two new methods of multi-sensor data fusion, based on low-cost Micro Electro-Mechanical Systems (MEMS) Inertial Sensors, to estimate joint angles of industrial manipulators are described in the fourth chapter. The results of two experimental tests are also presented to evaluate and to compare the performances of the two methods. A method to estimate the attitude and heading of an UAV is described in the fifth chapter. In order to check the performance of the developed method, at the end of the fifth chapter, a comparison test with a high accuracy system is presented. A localization algorithm for a wheeled UGV equipped with a GPS and an inertial platform is described in the sixth chapter. Simulation tests and experimental results are also presented at the end of the sixth chapter.

The aim of this thesis work is to investigate the possibility of applying a sensor fusion algorithm with a focus on estimating vehicle dynamic states, mainly the vehicle body accelerations. Modern passenger vehicles have several mechatronic systems such as active safety, comfort, driver assistance etc., which are highly dependant on accurate knowledge of such states. This work focuses on the mechatronic suspension system, which makes use of the body accelerations measurements to control the dynamics of the vehicle body in order to provide an improved driving experience. This work can be split up into two major parts, the first being the identification of available onboard sensors for measuring the vehicle body accelerations. Five different sensor combinations are considered and compared with each other. The next part is to develop a sensor fusion algorithm, in this case, a Kalman Filter (KF) based algorithm, which uses vehicle dynamic modelling knowledge to obtain accurate, reliable and less uncertain estimates of the states. Specifically, an Unscented Kalman Filter (UKF) and Cubature Kalman Filter (CKF) were built and compared with each other. Two different vehicle dynamic models, a vehicle planar dynamic model and a full car suspension model, were implemented to capture both the effects of road disturbances and drivingmanoeuvres on the vehicle body dynamics. Both these fusion algorithms were tested using simulation data and logged data and validated by comparing with an ideal sensing method to measure the body accelerations used currently at Volvo Car Corporation.
Syftet med detta examensarbete är att undersöka möjligheten att tillämpa en sensorfusionsalgoritm med fokus på att uppskatta fordonets dynamiska tillstånd, främst karossens acceleration. Moderna personbilar har flera mekatroniska system som aktiv säkerhet, komfort, förarassistans etc., som är mycket beroende av exakt kunskap om sådana tillstånd. Detta arbete fokuserar på det mekatroniska fjädringssystemet, som använder karossens accelerationsmätningar för att styra fordonets dynamik och för att ge en förbättrad körupplevelse. Detta arbete kan delas upp i två huvuddelar, den första är identifiering av tillgängliga inbyggda sensorer för mätning av fordonets accelerationer. Fem olika sensorkombinationer övervägs och jämförs med varandra. Nästa del är att utveckla en sensorfusionsalgoritm, i detta fall en kalmanfilter baserad algoritm, som använder kunskap om fordonets dynamik för att få exakta, pålitliga och mindre osäkra uppskattningar av tillstånden. Specifikt byggdes en UKF och CKF som jämfördes med varandra. Två olika fordonsdynamiska modeller, en plan dynamisk modell och en full hjulupphängningsmodell, implementerades för att fånga både effekterna av vägstörningar och körmanövrer på fordonets karossdynamik. Båda dessa fusionsalgoritmer testades med hjälp av simuleringsdata och loggade data och validerades genom att jämföra med en idealisk avkänningsmetod för att mäta karossaccelerationerna som används för närvarande på Volvo Car Corporation.

Due to rapid advancements in sensing and computation technology, multiple types of sensors have been embedded in various applications, on-line automatically collecting massive production information. Although this data-rich environment provides great opportunity for more effective process control, it also raises new research challenges on data analysis and decision making due to the complex data structures, such as heterogeneous data dependency, and large-volume and high-dimensional characteristics. This thesis contributes to the area of System Informatics and Control (SIAC) to develop systematic data fusion methodologies for effective quality control and performance improvement in complex systems. These advanced methodologies enable (1) a better handling of the rich data environment communicated by complex engineering systems, (2) a closer monitoring of the system status, and (3) a more accurate forecasting of future trends and behaviors. The research bridges the gaps in methodologies among advanced statistics, engineering domain knowledge and operation research. It also forms close linkage to various application areas such as manufacturing, health care, energy and service systems. This thesis started from investigating the optimal sensor system design and conducting multiple sensor data fusion analysis for process monitoring and diagnosis in different applications. In Chapter 2, we first studied the couplings or interactions between the optimal design of a sensor system in a Bayesian Network and quality management of a manufacturing system, which can improve cost-effectiveness and production yield by considering sensor cost, process change detection speed, and fault diagnosis accuracy in an integrated manner. An algorithm named “Best Allocation Subsets by Intelligent Search” (BASIS) with optimality proof is developed to obtain the optimal sensor allocation design at minimum cost under different user specified detection requirements. Chapter 3 extended this line of research by proposing a novel adaptive sensor allocation framework, which can greatly improve the monitoring and diagnosis capabilities of the previous method. A max-min criterion is developed to manage sensor reallocation and process change detection in an integrated manner. The methodology was tested and validated based on a hot forming process and a cap alignment process. Next in Chapter 4, we proposed a Scalable-Robust-Efficient Adaptive (SERA) sensor allocation strategy for online high-dimensional process monitoring in a general network. A monitoring scheme of using the sum of top-r local detection statistics is developed, which is scalable, effective and robust in detecting a wide range of possible shifts in all directions. This research provides a generic guideline for practitioners on determining (1) the appropriate sensor layout; (2) the “ON” and “OFF” states of different sensors; and (3) which part of the acquired data should be transmitted to and analyzed at the fusion center, when only limited resources are available. To improve the accuracy of remaining lifetime prediction, Chapter 5 proposed a data-level fusion methodology for degradation modeling and prognostics. When multiple sensors are available to measure the degradation mechanism of a same system, it becomes a high dimensional and challenging problem to determine which sensors to use and how to combine them together for better data analysis. To address this issue, we first defined two essential properties if present in a degradation signal, can enhance the effectiveness for prognostics. Then, we proposed a generic data-level fusion algorithm to construct a composite health index to achieve those two identified properties. The methodology was tested using the degradation signals of aircraft gas turbine engine, which demonstrated a much better prognostic result compared to relying solely on the data from an individual sensor. In summary, this thesis is the research drawing attention to the area of data fusion for effective employment of the underlying data gathering capabilities for system modeling, performance assessment and improvement. The fundamental data fusion methodologies are developed and further applied to various applications, which can facilitate resources planning, real-time monitoring, diagnosis and prognostics.

During this work, it was developed an algorithm of sensor fusion for mobile robot, an algorithm of obstacle avoidance called "Reactive Simulation", a localization algorithm environment referred based on LIght Detection And Ranging (LIDAR) data and an algorithm for object localization from range data.
Nel corso del presente lavoro è stato sviluppato e testato un algoritmo di sensor fusion per la navigazione inerziale-odometrica. Sensori come encoder e piattaforme inerziali basate su giroscopi hanno caratteristiche molto diverse in quanto ad accuratezza di misura in relazione alla manovra attuale compiuta da un veicolo autonomo. È quindi possibile utilizzare tali sensori per ottenere una maggior accuratezza nella stima della posa, e limitare la propagazione dell’incertezza a un primo livello di sensor fusion, a cui poi va aggiunto una ulteriore misura proveniente da un sensore riferito all’ambiente, in modo da ridurre periodicamente la propagazione dell’incertezza che cresce ad ogni ciclo di acquisizione e stima della posa a causa della correlazione temporale dei parametri. Nell’algoritmo proposto, gli encoder e il giroscopio vengono combinati tenendo in considerazione la rispettiva incertezza, stimata in funzione della manovra attuale che viene classificata a partire dai dati stessi. Un’altra parte del lavoro ha riguardato lo sviluppo di un algoritmo di localizzazione basato sul matching di scansioni ottenute da un sensore LIDAR, per disporre di una misura di posizione riferita all’ambiente, che può essere integrata in un algoritmo di sensor fusion o in algoritmi di SLAM (Simultaneous Localization And Mapping). Sempre basandosi sui dati provenienti dal LIDAR, è stato sviluppato un algoritmo di identificazione e localizzazione della posa di oggetti di forma nota descrivibili mediante un modello geometrico. Di entrambi è stata condotta un’analisi di incertezza utilizzando un setup realizzato allo scopo. È inoltre stato sviluppato un algoritmo real time di aggiramento ostacoli, denominato Reactive Simulation, che prende in considerazione la cinematica del veicolo, il modello dinamico e la sua incertezza, le condizioni iniziali, le misure dell’ambiente e l’incertezza dei sensori per calcolare la traiettoria che compierebbe il veicolo per raggiungere un target locale. Tale simulazione ha lo scopo di integrare una pianificazione dinamica della traiettoria, calcolando una predizione più accurata della traiettoria in presenza di ostacoli imprevisti e rendere più robusto l’aggiramento ostacoli. Tale algoritmo è stato implementato e ottimizzato per operare in real time su un robot mobile.

Physiological sensor signals analysis is common practice in medical domain for diagnosis andclassification of various physiological conditions. Clinicians’ frequently use physiologicalsensor signals to diagnose individual’s psychophysiological parameters i.e., stress tiredness,and fatigue etc. However, parameters obtained from physiological sensors could vary becauseof individual’s age, gender, physical conditions etc. and analyzing data from a single sensorcould mislead the diagnosis result. Today, one proposition is that sensor signal fusion canprovide more reliable and efficient outcome than using data from single sensor and it is alsobecoming significant in numerous diagnosis fields including medical diagnosis andclassification. Case-Based Reasoning (CBR) is another well established and recognizedmethod in health sciences. Here, an entropy based algorithm, “Multivariate MultiscaleEntropy analysis” has been selected to fuse multiple sensor signals. Other physiologicalsensor signals measurements are also taken into consideration for system evaluation. A CBRsystem is proposed to classify ‘healthy’ and ‘stressed’ persons using both fused features andother physiological i.e. Heart Rate Variability (HRV), Respiratory Sinus Arrhythmia (RSA),Finger Temperature (FT) features. The evaluation and performance analysis of the system have been done and the results ofthe classification based on data fusion and physiological measurements are presented in thisthesis work.

The usage of inertial sensors has traditionally been confined primarily to the aviation and marine industry due to their associated cost and bulkiness. During the last decade, however, inertial sensors have undergone a rather dramatic reduction in both size and cost with the introduction of MEMS technology. As a result of this trend, inertial sensors have become commonplace for many applications and can even be found in many consumer products, for instance smart phones, cameras and game consoles. Due to the drift inherent in inertial technology, inertial sensors are typically used in combination with aiding sensors to stabilize andimprove the estimates. The need for aiding sensors becomes even more apparent due to the reduced accuracy of MEMS inertial sensors. This thesis discusses two problems related to using inertial sensors in combination with aiding sensors. The first is the problem of sensor fusion: how to combine the information obtained from the different sensors and obtain a good estimate of position and orientation. The second problem, a prerequisite for sensor fusion, is that of calibration: the sensors themselves have to be calibrated and provide measurement in known units. Furthermore, whenever multiple sensors are combined additional calibration issues arise, since the measurements are seldom acquired in the same physical location and expressed in a common coordinate frame. Sensor fusion and calibration are discussed for the combination of inertial sensors with cameras, UWB or GPS. Two setups for estimating position and orientation in real-time are presented in this thesis. The first uses inertial sensors in combination with a camera; the second combines inertial sensors with UWB. Tightly coupled sensor fusion algorithms and experiments with performance evaluation are provided. Furthermore, this thesis contains ideas on using an optimization based sensor fusion method for a multi-segment inertial tracking system used for human motion capture as well as a sensor fusion method for combining inertial sensors with a dual GPS receiver. The above sensor fusion applications give rise to a number of calibration problems. Novel and easy-to-use calibration algorithms have been developed and tested to determine the following parameters: the magnetic field distortion when an IMU containing magnetometers is mounted close to a ferro-magnetic object, the relative position and orientation of a rigidly connected camera and IMU, as well as the clock parameters and receiver positions of an indoor UWB positioning system.
MATRIS (Markerless real-time Tracking for Augmented Reality Image), a sixth framework programme funded by the European Union
CADICS (Control, Autonomy, and Decision-making in Complex Systems), a Linneaus Center funded by the Swedish Research Council (VR)
Strategic Research Center MOVIII, funded by the Swedish Foundation for Strategic Research (SSF)

One of Saab Space’s products is the S19 guidance system for sounding rockets.Today this system is based on an inertial navigation system that blindly calculatesthe position of the rocket by integrating sensor readings with unknown bias. Thepurpose of this thesis is to integrate a Global Positioning System (GPS) receiverinto the guidance system to increase precision and robustness. There are mainlytwo problems involved in this integration. One is to integrate the GPS with sensorfusion into the existing guidance system. The seconds is to get the GPS satellitetracking to work under extremely high dynamics. The first of the two problems issolved by using an Extended Kalman filter (EKF) with two different linearizations.One of them is uses Euler angles and the other is done with quaternions. Theintegration technique implemented in this thesis is a loose integration between theGPS receiver and the inertial navigation system. The main task of the EKF isto estimate the bias of the inertial navigation system sensors and correct it toeliminate drift in the position. The solution is verified by computing the positionof a car using a GPS and an inertial measurement unit. Different solutions to theGPS tracking problem are proposed in a pre-study.

En av Saab Space produkter är navigationssystemet S19 som styr sondraketer.Fram till idag har systemet varit baserat på ett tröghetsnavigeringssystem somblint räknar ut position genom att integrera tröghetsnavigerinssystemets sensorermed okända biaser. Syftet med detta exjobb är att integrera en GPS med tröghetsnavigeringsystemetför att öka robusthet och precision. Det kan i huvudsak delasupp i två problem; att integrera en GPS-mottagare med det befintliga navigationsystemetmed användning utav sensorfusion, och att få satellitföljningen attfungera under extremt höga dynamiska förhållanden. Det första av de två problemenlöses genom ett Extended Kalman filter (EKF) med två olika linjäriseringar.Den första linjäriseringen är med Eulervinklar och är välbeprövad. Den andra ärmed kvaternioner. Integrationstekniken som implementeras i detta Examensarbeteär en lös integration mellan GPS-mottagaren och tröghetsnavigeringssystemet. Huvudsyftetmed EKF:en är att estimera bias i tröghetsnavigeringsystemets sensoreroch korrigera dem för att eliminera drifter i position. Lösningen verifieras genomatt räkna ut positionen för en bil med GPS och en inertiell mätenhet. Olika lösningartill satellitföljningen föreslås i en förstudie.

A lane departure warning system relying exclusively on a camera has several shortcomings and tends to be sensitive to, e.g., bad weather and abrupt manoeuvres. To handle these situations, the system proposed in this thesis uses a dynamic model of the vehicle and integration of relative motion sensors to estimate the vehicle’s position on the road. The relative motion is measured using vision, inertial, and vehicle sensors. All these sensors types are affected by errors such as offset, drift and quantization. However the different sensors are sensitive to different types of errors, e.g., the camera system is rather poor at detecting rapid lateral movements, a type of situation which an inertial sensor practically never fails to detect. These kinds of complementary properties make sensor fusion interesting. The approach of this Master’s thesis is to use an already existing lane departure warning system as vision sensor in combination with an inertial measurement unit to produce a system that is robust and can achieve good warnings if an unintentional lane departure is about to occur. For the combination of sensor data, different sensor fusion models have been proposed and evaluated on experimental data. The models are based on a nonlinear model that is linearized so that a Kalman filter can be applied. Experiments show that the proposed solutions succeed at handling situations where a system relying solely on a camera would have problems. The results from the testing show that the original lane departure warning system, which is a single camera system, is outperformed by the suggested system.

The aim of platooning is to enable several Heavy Duty Vehicles (HDVs) to drive in a convoy and act as one unit to decrease the fuel consumption. By introducing wireless communication and tight control, the distance between the HDVs can be decreased significantly. This implies a reduction of the air drag and consequently the fuel consumption for all the HDVs in the platoon. The challenge in platooning is to keep the HDVs as close as possible to each other without endangering safety. Therefore, sensor fusion is necessary to get an accurate estimate of the relative distance and velocity, which is a pre-requisite for the controller. This master thesis aims at developing a sensor fusion framework from on-board sensor information as well as other vehicles’ sensor information communicated over a WiFi link. The most important sensors are GPS, that gives a rough position of each HDV, and radar that provides relative distance for each pair of HDV’s in the platoon. A distributed solution is developed, where an Extended Kalman Filter (EKF) estimates the state of the whole platoon. The state vector includes position, velocity and length of each HDV, which is used in a Model Predictive Control (MPC). Furthermore, a method is discussed on how to handle vehicles outside the platoon and how various road surfaces can be managed. This master thesis is a part of a project consisting of three parallel master’s theses. The other two master’s theses investigate and implement rough pre-processing of data, time synchronization and MPC associated with platooning. It was found that the three implemented systems could reduce the average fuel consumption by 11.1 %.

The fields of navigation and motion inference have rapidly been transformed by advances in computing, connectivity, and sensor design. As a result, unprecedented amounts of data are today being collected by cheap and small navigation sensors residing in our surroundings. Often, these sensors will be embedded into personal mobile devices such as smartphones and tablets. To transform the collected data into valuable information, one must typically formulate and solve a statistical inference problem. This thesis is concerned with inference problems that arise when trying to use smartphone sensors to extract information on driving behavior and traffic conditions. One of the fundamental differences between smartphone-based driver behavior profiling and traditional analysis based on vehicle-fixed sensors is that the former is based on measurements from sensors that are mobile with respect to the vehicle. Thus, the utility of data from smartphone-embedded sensors is diminished by not knowing the relative orientation and position of the smartphone and the vehicle. The problem of estimating the relative smartphone-to-vehicle orientation is solved by extending the state-space model of a global navigation satellite system-aided inertial navigation system. Specifically, the state vector is augmented to include the relative orientation, and the measurement vector is augmented with pseudo observations describing well-known characteristics of car dynamics. To estimate the relative positions of multiple smartphones, we exploit the kinematic relation between the accelerometer measurements from different smartphones. The characteristics of the estimation problem are examined using the Cramér-Rao bound, and the positioning method is evaluated in a field study using concurrent measurements from seven smartphones. The characteristics of smartphone data vary with the smartphone's placement in the vehicle. To investigate this, a large set of vehicle trip segments are clustered based on measurements from smartphone-embedded sensors and vehicle-fixed accelerometers. The clusters are interpreted as representing the smartphone being rigidly mounted on a cradle, placed on the passenger seat, held by hand, etc. Finally, the problem of fusing speed measurements from the on-board diagnostics system and a global navigation satellite system receiver is considered. Estimators of the vehicle’s speed and the scale factor of the wheel speed sensors are derived under the assumptions of synchronous and asynchronous samples.

QC 20171123

Existing techniques for sensor validation and sensor fusion are often based on analytical sensor models. Such models can be arbitrarily complex and consequently Gaussian distributions are often assumed, generally with a detrimental effect on overall system performance. A holistic approach has therefore been adopted in order to develop two novel and complementary approaches to sensor validation and fusion based on empirical data. The first uses the Nadaraya-Watson kernel estimator to provide competitive sensor fusion. The new algorithm is shown to reliably detect and compensate for bias errors, spike errors, hardover faults, drift faults and erratic operation, affecting up to three of the five sensors in the array. The inherent smoothing action of the kernel estimator provides effective noise cancellation and the fused result is more accurate than the single 'best sensor'. A Genetic Algorithm has been used to optimise the Nadaraya-Watson fuser design. The second approach uses analytical redundancy to provide the on-line sensor status output μH∈[0,1], where μH=1 indicates the sensor output is valid and μH=0 when the sensor has failed. This fuzzy measure is derived from change detection parameters based on spectral analysis of the sensor output signal. The validation scheme can reliably detect a wide range of sensor fault conditions. An appropriate context dependent fusion operator can then be used to perform competitive, cooperative or complementary sensor fusion, with a status output from the fuser providing a useful qualitative indication of the status of the sensors used to derive the fused result. The operation of both schemes is illustrated using data obtained from an array of thick film metal oxide pH sensor electrodes. An ideal pH electrode will sense only the activity of hydrogen ions, however the selectivity of the metal oxide device is worse than the conventional glass electrode. The use of sensor fusion can therefore reduce measurement uncertainty by combining readings from multiple pH sensors having complementary responses. The array can be conveniently fabricated by screen printing sensors using different metal oxides onto a single substrate.

The main motivation of this thesis is to design low-complexity high efficiency noncoherent fusion rules for the parallel triple-layer wireless sensor networks (WSNs) based on frequency-hopping Mary frequency shift keying (FH/MFSK) techniques, which are hence referred to as the FH/MFSK WSNs. The FH/MFSKWSNs may be employed to monitor single or multiple source events (SEs)with each SE having multiple states. In the FH/MFSKWSNs, local decisions made by local sensor nodes (LSNs) are transmitted to a fusion center (FC) with the aid of FH/MFSK techniques. At the FC, various noncoherent fusion rules may be suggested for final detection (classification) of the SEs’ states. Specifically, in the context of the FH/MFSK WSNs monitoring single M-ary SE, three noncoherent fusion rules are considered for fusion detection, which include the benchmark equal gain combining (EGC), and the proposed erasure-supported EGC (ES-EGC) as well as the optimum posterior fusion rules. Our studies demonstrate that the ES-EGC fusion rule may significantly outperform the EGC fusion rule, in the cases when the LSNs’ detection is unreliable and when the channel signal-to-noise ratio (SNR) is relative high. For the FH/MFSKWSNs monitoring multiple SEs, six noncoherent fusion rules are investigated, which include the EGC, ES-EGC, EGC assisted N-order IIC (EGC-NIIC), ES-EGC assisted N-order IIC (ES-EGC-NIIC), EGC assisted r-order IIC (EGC-rIIC) and the ES-EGC assisted r-order IIC (ES-EGC-rIIC). The complexity, characteristics as well as detection performance of these fusion rules are investigated. Our studies show that the ES-EGC related fusion rules are highly efficient fusion rules, which have similar complexity as the corresponding EGC related fusion rules, but usually achieve better detection performance than the EGC related fusion rules. Although the ES-EGC is a single-user fusion rule, it is however capable of mitigating the multiple event interference (MEI) generated by multiple SEs. Furthermore, in some of the considered fusion rules, the embedded parameters may be optimized for the FH/MFSK WSNs to achieve the best detection performance. As soft-sensing is often more reliable than hard-sensing, in this thesis, the FH/MFSK WSNs with the LSNs using soft-sensing are investigated associated with the EGC and ES-EGC fusion rules. Our studies reveal that the ES-EGC becomes highly efficient, when the sensing at LSNs is not very reliable. Furthermore, as one of the applications, our FH/MFSK WSN is applied for cognitive spectrum sensing of a primary radio (PR) system constituted by the interleaved frequencydivision multiple access (IFDMA) scheme, which supports multiple uplink users. Associated with our cognitive spectrum sensing system, three types of energy detection based sensing schemes are addressed, and four synchronization scenarios are considered to embrace the synchronization between the received PR IFDMA signals and the sampling operations at cognitive spectrum sensing nodes (CRSNs). The performance of the FH/MFSK WSN assisted spectrum sensing system with EGC or ES-EGC fusion rule is investigated. Our studies show that the proposed spectrum sensing system constitutes one highly reliable spectrum sensing scheme, which is capable of exploiting the space diversity provided by CRSNs and the frequency diversity provided by the IFDMA systems. Finally, the thesis summarises our discoveries and provides discussion on the possible future research issues.

Thesis (M.S. in Combat Systems Technology)--Naval Postgraduate School, Dec. 2004.
Thesis Advisor(s): Monique P. Fargues, Alfred W. Cooper. Includes bibliographical references (p. 73-75). Also available online.

The concept of consensus filters for sensor fusion is not an entirely new proposition but one with an internally implemented Bayesian fusion is. This work documents a novel state update algorithm for sensor fusion which works using the principle of Bayesian fusion of data with variance implemented on a single integrator consensus algorithm. Comparative demonstrations of how consensus over a pinning network is reached are presented along with a weighted Bayesian Luenberger type observer and a ’Consensus on estimates’ algorithm. This type of a filter is something that is novel and has not been encountered in previous literature related to this topic to the best of our knowledge. In this work, we also extend the proof for a distributed Luenberger type observer design to include the case where the network being considered is a strongly connected digraph.

Recent advances in the telecommunications industry and the resulting applicationssuch as autonomous vehicles, vehicle surveillance and traffic safetyhas increased the demand for accurate and robust vehicle positioning systems.Existing Global Navigation Satellite System (GNSS) based positioning techniquesface significant performance loss in the tunnels and urban canyons.Recent researches have shown that radio-based positioning techniques are theoreticallypromising to make an accurate navigation system to fill the GNSSgaps. Fifth generation of mobile communication (5G) will utilize wide bandwidthstogether with beamforming enabled by antenna arrays to provide higherdata rates to mobile users. These features make 5G a favorable candidate forhigh accuracy positioning. On the other hand, sensor fusion is commonly employedto develop more robust and accurate navigation systems for vehicles. Inthis work, the range and angle measurements from 5G base stations are fusedwith the acceleration measurements by the means of the extended Kalman filterto generate position estimates for a moving car. The accuracy of this positioningsystem is studied with centimeter wave (cmWave) and millimeter wave(mmWave) 5G cellular networks which are set up by practical parameters. Towardsthat, the positioning system is tested in a simulation-based experimentwhere a car is moving on a highway and the 5G base stations are deployedalongside of it. Based on that, a detailed analysis of the Kalman filter’s rootmean squared error (RMSE) and the 5G’s different parameters and limitingfactors such as the line of sight (LOS) blockage is carried out. Our numericalresults show that vehicles connected to 5G can benefit from this system to enhancethe robustness and accuracy of their navigation system.
De senaste framstegen inom telekommunikationsindustrin och de resulterandeapplikationerna som autonoma fordon, fordonsövervakning och trafiksäkerhethar ökat efterfrågan på exakta fordonspositioneringssystem. ExisterandeGlobal Navigation Satellite System (GNSS) baserade positioneringsteknikerhar en betydande prestandaförlust i tunnlar och urbana kanjoner. Forskninghar visat att radiobaserade positioneringstekniker har mindre distributionskostnaderoch kan vara mer exakta än satellitbaserade navigationssystem.I den femte generation av mobilkommunikation (5G) används tekniker sommillimeterWave (mmWave) och multiple-input multiple-output (MIMO) därradio-terminaler består av stora matrisantenner och arbetar med stora bandbredder.Dessa funktioner gör 5G-system gynnsamma för positionering medhög noggrannhet. Å andra sidan har informationsfusion av Inertial NavigationSystems (INS) och andra positioneringstekniker vanligen använts för attutveckla mer robusta och exakta spårningssystem. I denna studie föreslår viett INS/5G-positioneringssystem för att spåra landfordon baserat på Kalmanfiltret. Vi adresserar systempositioneringsgränserna i termer av 5G nya radio(NR) subsystem och en detaljerad analys av beroendet av rotmedelfelteradkvadratfel (RMSE) för olika systemparametrar som utförs. Systemet testas iett enkelt simuleringsbaserat experiment som består av en rak motorväg medbasstationerna placerade bredvid det. Slutligen visar våra numeriska resultatatt det föreslagna systemet är i stånd att lokalisera ett UE-monterat fordon medsub-meter lägesfel även i närvaro av hård siktlinje blockering.

During the last decades, great steps have been taken to decrease passenger fatality in cars. Systems such as ABS and airbags have been developed for this purpose alone. But not much effort has been put into pedestrian safety. In traffic today, pedestrians are one of the most endangered participants and in recent years, there has been an increased demand for pedestrian safety from the European Enhanced Vehicle safety Committee and the European New Car Assessment Programme has thereby developed tests where pedestrian safety is rated. With this, detection of pedestrians has arised as a part in the automotive safety research.

This thesis provides some of this research available in the area and a brief introduction to some of the sensors readily available. The objective of this work is to detect pedestrians in front of a vehicle by using thermoelectric infrared sensors fused with short range radar sensors and also to minimize any missed detections or false alarms. There has already been extensive work performed with the thermoelectric infrared sensors for this sole purpose and this thesis is based on that work.

Information is provided about the sensors used and an explanation of how they are set up during this work. Methods used for classifying objects are given and the assumptions made about pedestrians in this system. A basic tracking algorithm is used to track radar detected objects in order to provide the fusion system with better data. The approach chosen for the sensor fusion is a central-level fusion where the probabilities for a pedestrian from the radars and the thermoelectric infrared sensors are combined using Dempster-Shafer Theory and accumulated over time in the Occupancy Grid framework. Theories that are extensively used in this thesis are explained in detail and discussed accordingly in different chapters.

Finally the experiments undertaken and the results attained from the presented system are shown. A comparison is made with the previous detection system, which only uses thermoelectric infrared sensors and of which this work continues on. Conclusions regarding what this system is capable of are drawn with its inherent strengths and weaknesses.

This project examines the level of accuracy that can be achieved in precision positioning by using built-in sensors in an Android smartphone. The project is focused in estimating the position of the phone inside a building where the GPS signal is bad or unavailable. The approach is sensor-fusion: by using data from the device’s different sensors, such as accelerometer, gyroscope and wireless adapter, the position is determined. The results show that the technique is promising for future handheld indoor navigation systems that can be used in malls, museums, large office buildings, hospitals, etc.

The automotive industry has currently a high focus on automating road vehicles. Positive environmental impact can be achieved if carsharing becomes more common, aiding fewer cars on the roads. When the human factor in driving decreases, positive effects may be seen in traffic safety. But many challanges remain, for example the questions of liability. The vehicles must be able to detect their surroundings and the sensors need redundancy. Sensor fusion techniques increase the reliability of measurement results by combining measurement results from multiple different sensors. This thesis uses inertial sensors to calculate position and heading. An unscented Kalman filter has been designed and implemented on a demonstrator. The demonstrator consists of an r/c car with autonomous functions. It has a forward-facing camera and it can follow road sidelines. The Kalman filter incorporates measurements from two incremental encoders, a gyroscope and a steering angle sensor. The result shows that the combination of sensor measurements provides a better estimation of position and direction of travel.
The automotive industry has currently a high focus on automating road vehicles. Positive environmental impact can be achieved if carsharing becomes more common, aiding fewer cars on the roads. When the human factor in driving decreases, positive effects may be seen in traffic safety. But many challanges remain, for example the questions of liability. The vehicles must be able to detect their surroundings and the sensors need redundancy. Sensor fusion techniques increase the reliability of measurement results by combining measurement results from multiple different sensors. This thesis uses inertial sensors to calculate position and heading. An unscented Kalman filter has been designed and implemented on a demonstrator. The demonstrator consists of an r/c car with autonomous functions. It has a forward-facing camera and it can follow road sidelines. The Kalman filter incorporates measurements from two incremental encoders, a gyroscope and a steering angle sensor. The result shows that the combination of sensor measurements provides a better estimation of position and direction of travel.
Fordonsindustrin har högt fokus på att automatisera vägfordon. Positiv miljöpåverkan kan uppstå om bildelning blir vanligare och om det blir mindre bilar på vägarna. När den mänskliga faktorn minskar så tillåts högre trafiksäkerhet. Men många tekniska utmaningar kvarstår, speciellt kring ansvarfrågor. Fordonen måste kunna känna av sin omgivning och sensorerna behöver redundans. Sensor fusion tekniker ökar pålitligheten av mätresultat genom att kombinera mätresultat från flera olika sensorer. Detta examensarbete använder tröghetssensorer för att beräkna position och körriktning. Ett unscented Kalman filter har designats och implementerats på en demonstrator. Demonstratorn består av en radiostyrd bil med autonoma funktioner. Den har en framåt-riktad kamera och kan följa väglinjer. Kalman filtret tar in mätresultat från två vinkelgivare, ett gyroskåp och en styrvinkel sensor. Resultatet visar att kombinationen av sensorerna ger en bättre estimering av position och körriktning.

Progressive developments in computing and sensor technologies during the past decades have enabled the formulation of increasingly advanced problems in statistical inference and signal processing. The thesis is concerned with statistical estimation methods, and is divided into three parts with focus on two different areas: sensor fusion and sparse signal processing. The first part introduces the well-established Bayesian, Fisherian and least-squares estimation frameworks, and derives new estimators. Specifically, the Bayesian framework is applied in two different classes of estimation problems: scenarios in which (i) the signal covariances themselves are subject to uncertainties, and (ii) distance bounds are used as side information. Applications include localization, tracking and channel estimation. The second part is concerned with the extraction of useful information from multiple sensors by exploiting their joint properties. Two sensor configurations are considered here: (i) a monocular camera and an inertial measurement unit, and (ii) an array of passive receivers. New estimators are developed with applications that include inertial navigation, source localization and multiple waveform estimation. The third part is concerned with signals that have sparse representations. Two problems are considered: (i) spectral estimation of signals with power concentrated to a small number of frequencies,and (ii) estimation of sparse signals that are observed by few samples, including scenarios in which they are linearly underdetermined. New estimators are developed with applications that include spectral analysis, magnetic resonance imaging and array processing.

QC 20130426

One of the main tasks for an industrial robot is to move the end-effector in a predefined path with a specified velocity and acceleration. Different applications have different requirements of the performance. For some applications it is essential that the tracking error is extremely small, whereas other applications require a time optimal tracking. Independent of the application, the controller is a crucial part of the robot system. The most common controller configuration uses only measurements of the motor angular positions and velocities, instead of the position and velocity of the end-effector. The development of new cost optimised robots has introduced unwanted flexibilities in the joints and the links. The consequence is that it is no longer possible to get the desired performance and robustness by only measuring the motor angular positions.  This thesis investigates if it is possible to estimate the end-effector position using Bayesian estimation methods for state estimation, here represented by the extended Kalman filter and the particle filter. The arm-side information is provided by an accelerometer mounted at the end-effector. The measurements consist of the motor angular positions and the acceleration of the end-effector. In a simulation study on a realistic flexible industrial robot, the angular position performance is shown to be close to the fundamental Cramér-Rao lower bound. The methods are also verified in experiments on an ABB IRB4600 robot, where the dynamic performance of the position for the end-effector is significantly improved. There is no significant difference in performance between the different methods. Instead, execution time, model complexities and implementation issues have to be considered when choosing the method. The estimation performance depends strongly on the tuning of the filters and the accuracy of the models that are used. Therefore, a method for estimating the process noise covariance matrix is proposed. Moreover, sampling methods are analysed and a low-complexity analytical solution for the continuous-time update in the Kalman filter, that does not involve oversampling, is proposed.  The thesis also investigates two types of control problems. First, the norm-optimal iterative learning control (ILC) algorithm for linear systems is extended to an estimation-based norm-optimal ILC algorithm where the controlled variables are not directly available as measurements. The algorithm can also be applied to non-linear systems. The objective function in the optimisation problem is modified to incorporate not only the mean value of the estimated variable, but also information about the uncertainty of the estimate. Second, H∞ controllers are designed and analysed on a linear four-mass flexible joint model. It is shown that the control performance can be increased, without adding new measurements, compared to previous controllers. Measuring the end-effector acceleration increases the control performance even more. A non-linear model has to be used to describe the behaviour of a real flexible joint. An H∞-synthesis method for control of a flexible joint, with non-linear spring characteristic, is therefore proposed.
En av de viktigaste uppgifterna för en industrirobot är att förflytta verktyget i en fördefinierad bana med en specificerad hastighet och acceleration. Exempel på användningsområden för en industrirobot är bland annat bågsvetsning eller limning. För dessa typer av applikationer är det viktigt att banföljningsfelet är extremt litet, men även hastighetsprofilen måste följas så att det till exempel inte appliceras för mycket eller för lite lim. Andra användningsområden kan vara punktsvetsning av bilkarosser och paketering av olika varor. För dess applikationer är banföljningen inte det viktiga, istället kan till exempel en tidsoptimal banföljning krävas eller att svängningarna vid en inbromsning minimeras. Oberoende av applikationen är regulatorn en avgörande del av robotsystemet. Den vanligaste regulatorkonfigurationen använder bara mätningar av motorernas vinkelpositioner och -hastigheter, istället för positionen och hastigheten för verktyget, som är det man egentligen vill styra.  En del av utvecklingsarbetet för nya generationers robotar är att reducera kostnaden men samtidigt förbättra prestandan. Ett sätt att minska kostnaden kan till exempel vara att minska dimensionerna på länkarna eller köpa in billigare växellådor. Den här utvecklingen av kostnadsoptimerade robotar har infört oönskade flexibiliteter i leder och länkar. Det är därför inte längre möjligt att få den önskade prestandan och robustheten genom att bara mäta motorernas vinkelpositioner och -hastigheter. Istället krävs det omfattande matematiska modeller som beskriver dessa oönskade flexibiliteter. Dessa modeller kräver mycket arbete att dels ta fram men även för att identifiera parametrarna. Det finns automatiska metoder för att beräkna modellparametrarna men oftast krävs det en manuell justering för att få bra prestanda.  Den här avhandlingen undersöker möjligheterna att beräkna verktygspositionen med hjälp av bayesianska metoder för tillståndsskattning. De bayesianska skattningsmetoderna beräknar tillstånden för ett system iterativt. Med hjälp av en matematisk modell över systemet predikteras vad tillståndet ska vara vid nästa tidpunkt. Efter att mätningar av systemet vid den nya tidpunkten har genomförts justeras skattningen med hjälp av dessa mätningar. De metoder som har använts i avhandlingen är det så kallade extended Kalman filtret samt partikelfiltret.  Informationen på armsidan av växellådan ges av en accelerometer som är monterad på verktyget. Med hjälp av accelerationen för verktyget och motorernas vinkelpositioner kan en skattning av verktygspositionen beräknas. I en simuleringsstudie för en realistisk vek robot har det visats att skattningsprestandan ligger nära den teoretiska undre gränsen, känd som Raooch mätstörningar som påverkar roboten. För att underlätta trimningen så har en metod för att skatta processbrusets kovariansmatris föreslagits. En annan viktig del som påverkar prestandan är modellerna som används i filtren. Modellerna för en industrirobot är vanligtvis framtagna i kontinuerlig tid medan filtren använder modeller i diskret tid. För att minska felen som uppkommer då de tidskontinuerliga modellerna överförs till diskret tid har olika samplingsmetoder studerats. Vanligtvis används enkla metoder för att diskretisera vilket innebär problem med prestanda och stabilitet. För att hantera dessa problem införs översampling vilket innebär att tidsuppdateringen sker med en mycket kortare sampeltid än vad mätuppdateringen gör. För att undvika översampling kan det motsvarande tidskontinuerliga filtret användas för att prediktera tillstånden vid nästa diskreta tidpunkt. En analytisk lösning med låg beräkningskomplexitet till detta problem har föreslagits.  Vidare innehåller avhandlingen två typer av reglerproblem relaterade till industrirobotar. För det första har den så kallade norm-optimala iterative learning control styrlagen utökats till att hantera fallet då en skattning av den önskade reglerstorheten används istället för en mätning. Med hjälp av skattningen av systemets tillståndsvektor kan metoden nu även användas till olinjära system vilket inte är fallet med standardformuleringen. Den föreslagna metoden utökar målfunktionen i optimeringsproblemet till att innehålla inte bara väntevärdet av den skattade reglerstorheten utan även skattningsfelets kovariansmatris. Det innebär att om skattningsfelet är stort vid en viss tidpunkt ska den skattade reglerstorheten vid den tidpunkten inte påverka resultatet mycket eftersom det finns en stor osäkerhet i var den sanna reglerstorheten befinner sig.  För det andra har design och analys av H∞-regulatorer för en linjär modell av en vek robotled, som beskrivs med fyra massor, genomförts. Det visar sig att reglerprestandan kan förbättras, utan att lägga till fler mätningar än motorns vinkelposition, jämfört med tidigare utvärderade regulatorer. Genom att mäta verktygets acceleration kan prestandan förbättras ännu mer. Modellen över leden är i själva verket olinjär. För att hantera detta har en H∞-syntesmetod föreslagits som kan hantera olinjäriteten i modellen.
Vinnova Excellence Center LINK-SIC

Engineers and infrastructure owners have to manage an aging civil infrastructure in the US. Engineers have the opportunity to analyze structures using finite element models (FEM), and often base their engineering decisions on the outcome of the results. Ultimately, the success of these decisions is directly related to the accuracy of the finite element model in representing the real-life structure. Improper assumptions in the model such as member properties or connections, can lead to inaccurate results. A major source of modeling error in many finite element models of existing structures is due to improper representation of the boundary conditions. In this study, it is aimed to integrate experimental and analytical concepts by means of parameter estimation, whereby the boundary condition parameters of a structure in question are determined. FEM updating is a commonly used method to determine the "as-is" condition of an existing structure. Experimental testing of the structure using static and/or dynamic measurements can be utilized to update the unknown parameters. Optimization programs are used to update the unknown parameters by minimizing the error between the analytical and experimental measurements. Through parameter estimation, unknown parameters of the structure such as stiffness, mass or support conditions can be estimated, or more appropriately, "updated", so that the updated model provides for a better representation of the actual conditions of the system. In this study, a densely instrumented laboratory test beam was used to carry-out both analytical and experimental analysis of multiple boundary condition setups. The test beam was instrumented with an array of displacement transducers, tiltmeters and accelerometers. Linear vertical springs represented the unknown boundary stiffness parameters in the numerical model of the beam. Nine different load cases were performed and static measurements were used to update the spring stiffness, while dynamic measurements and additional load cases were used to verify these updated parameters. Two different optimization programs were used to update the unknown parameters and then the results were compared. One optimization tool was developed by the author, Spreadsheet Parameter Estimation (SPE), which utilized the Solver function found in the widely available Microsoft Excel software. The other one, comprehensive MATLAB-based PARameter Identification System (PARIS) software, was developed at Tufts University. Optimization results from the two programs are presented and discussed for different boundary condition setups in this thesis. For this purpose, finite element models were updated using the static data and then these models were checked against dynamic measurements for model validation. Model parameter updating provides excellent insight into the behavior of different boundary conditions and their effect on the overall structural behavior of the system. Updated FEM using estimated parameters from both optimization software programs generally shows promising results when compared to the experimental data sets. Although the use of SPE is simple and generally straight-forward, we will see the apparent limitations when dealing with complex, non-linear support conditions. Due to the inherent error associated with experimental measurements and FEM modeling assumptions, PARIS serves as a better suited tool to perform parameter estimation. Results from SPE can be used for quick analysis of structures, and can serve as initial inputs for the more in depth PARIS models. A number of different sensor types and spatial resolution were also investigated for the possible minimum instrumentation to have an acceptable model representation in terms of model and experimental data correlation.
M.S.
Department of Civil and Environmental Engineering
Engineering and Computer Science
Civil Engineering MS

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.
Includes bibliographical references (leaves 81-82).
by Steve J. Steiner.
M.Eng.

With the purpose of classifying and modelling stress, different sensors, signal features, machine learning methods, and stress experiments have been compared. Two databases have been studied: the MIT driver stress database and a new experimental database, where three stress tasks have been performed for 9 subjects: the Trier Social Stress Test, the Socially Evaluated Cold Pressor Test and the d2 test, of which the latter is not classically used for generating stress. Support vector machine, naive Bayes, k-nearest neighbor and probabilistic neural network classification techniques were compared, with support vector machines achieving the highest performance in general (99.5 ±0.6 %$on the driver database and 91.4 ± 2.4 % on the experimental database). For both databases, relevant features include the mean of the heart rate and the mean of the galvanic skin response, together with the mean of the absolute derivative of the galvanic skin response signal. A new feature is also introduced with great performance in stress classification for the driver database. Continuous models for estimating stress levels have also been developed, based upon the perceived stress levels given by the subjects during the experiments, where support vector regression is more accurate than linear and variational Bayesian regression.
I syfte att klassificera och modellera stress har olika sensorer, signalegenskaper, maskininlärningsmetoder och stressexperiment jämförts. Två databaser har studerats: MIT:s förarstressdatabas och en ny databas baserad på egna experiment, där stressuppgifter har genomförts av nio försökspersoner: Trier Social Stress Test,  Socially Evaluated Cold Pressor Test och d2-testet, av vilka det sistnämnda inte normalt används för att generera stress. Support vector machine-, naive Bayes-, k-nearest neighbour- och probabilistic neural network-algoritmer har jämförts, av vilka support vector machine har uppnått den högsta prestandan i allmänhet (99.5 ± 0.6 % på förardatabasen, 91.4 ± 2.4 %  på experimenten). För båda databaserna har signalegenskaper såsom medelvärdet av hjärtrytmen och hudens ledningsförmåga, tillsammans med medelvärdet av beloppet av hudens ledningsförmågas derivata identifierats som relevanta. En ny signalegenskap har också introducerats, med hög prestanda i stressklassificering på förarstressdatabasen. En kontinuerlig modell har också utvecklats, baserad på den upplevda stressnivån angiven av försökspersonerna under experimenten, där support vector regression har uppnått bättre resultat än linjär regression och variational Bayesian regression.

Manufacturing companies of today face unpredictable, high frequency market changes driven by global competition. To stay competitive, these companies must have the characteristics of cost-effective rapid response to the market needs. As an engineering discipline, mechatronics strives to integrate mechanical, electronic, and computer systems optimally in order to create high precision products and manufacturing processes. This paper presents a methodology of increasing flexibility and reusability of a generic computer integrated manufacturing (CIM) cell-control system using simulation and modelling of mechatronic sensory system (MSS) concepts. The utilisation of sensors within the CIM cell is highlighted specifically for data acquisition, analysis, and multi-sensor data fusion. Thus the designed reference architecture provides comprehensive insight for the functions and methodologies of a generic shop-floor control system (SFCS), which consequently enables the rapid deployment of a flexible system.

In recent years, inertial sensors have undergone major developments. The quality of their measurements has improved while their cost has decreased, leading to an increase in availability. They can be found in stand-alone sensor units, so-called inertial measurement units, but are nowadays also present in for instance any modern smartphone, in Wii controllers and in virtual reality headsets. The term inertial sensor refers to the combination of accelerometers and gyroscopes. These measure the external specific force and the angular velocity, respectively. Integration of their measurements provides information about the sensor's position and orientation. However, the position and orientation estimates obtained by simple integration suffer from drift and are therefore only accurate on a short time scale. In order to improve these estimates, we combine the inertial sensors with additional sensors and models. To combine these different sources of information, also called sensor fusion, we make use of probabilistic models to take the uncertainty of the different sources of information into account. The first contribution of this thesis is a tutorial paper that describes the signal processing foundations underlying position and orientation estimation using inertial sensors. In a second contribution, we use data from multiple inertial sensors placed on the human body to estimate the body's pose. A biomechanical model encodes the knowledge about how the different body segments are connected to each other. We also show how the structure inherent to this problem can be exploited. This opens up for processing long data sets and for solving the problem in a distributed manner. Inertial sensors can also be combined with time of arrival measurements from an ultrawideband (UWB) system. We focus both on calibration of the UWB setup and on sensor fusion of the inertial and UWB measurements. The UWB measurements are modeled by a tailored heavy-tailed asymmetric distribution. This distribution naturally handles the possibility of measurement delays due to multipath and non-line-of-sight conditions while not allowing for the possibility of measurements arriving early, i.e. traveling faster than the speed of light. Finally, inertial sensors can be combined with magnetometers. We derive an algorithm that can calibrate a magnetometer for the presence of metallic objects attached to the sensor. Furthermore, the presence of metallic objects in the environment can be exploited by using them as a source of position information. We present a method to build maps of the indoor magnetic field and experimentally show that if a map of the magnetic field is available, accurate position estimates can be obtained by combining inertial and magnetometer measurements.

The development of completely autonomous mobile vehicles has been the topic of a great deal of research over the past few decades. Spurred by interests as diverse as space exploration and land mine removal, research has focused on the mechanical requirements, sensing and computational requirements, and intelligence required for autonomous decision making. This thesis focuses on developing the software required for autonomous control, while building upon previous research into appropriate mechanical designs and sensing technologies. The thesis begins by giving an overview of the problem, and then moves on to reviewing the literature relevant to the task of fusing diverse, and often conflicting, sensor data into a usable representation. Literature relevant to the task of using that data to make intelligent decisions in an autonomous manner is reviewed. The focus then shifts to developing a working platform, called Navigator, which tests the theory in the setting of the Intelligent Ground Vehicle Competition. The theory required to control Navigator, along with the dynamic analysis used for controls testing, is developed. Image processing techniques useful for extracting features from the course are discussed, and the required mathematical relationships are derived. The thesis then discusses modifications to the Vector Field Histogram technique, which enable Navigator to fuse data from both the image processing and laser rangefinder. Development of the navigation decision-making algorithm is discussed. The information in this thesis is presented in such a way that it can serve as a reference to those who follow in the task of building autonomous vehicles.
Master of Science

The importance of travel time estimation has increased due to the central role it plays in a number of emerging intelligent transport systems and services including Advanced Traveller Information Systems (ATIS), Urban Traffic Control (UTC), Dynamic Route Guidance (DRG), Active Traffic Management (ATM), and network performance monitoring. Along with the emerging of new sensor technologies, the much greater volumes of near real time data provided by these new sensor systems create opportunities for significant improvement in travel time estimation. Data fusion as a recent technique leads to a promising solution to this problem. This thesis presents the development and testing of new methods of multi-sensor data fusion for the accurate, reliable and robust estimation of travel time. This thesis reviews the state-of-art data fusion approaches and its application in transport domain, and discusses both of opportunities and challenging of applying data fusion into travel time estimation in a heterogeneous real time data environment. For a particular England highway scenario where ILDs and ANPR data are largely available, a simple but practical fusion method is proposed to estimate the travel time based on a novel relationship between space-mean-speed and time-mean-speed. In developing a general fusion framework which is able to fuse ILDs, GPS and ANPR data, the Kalman filter is identified as the most appropriate fundamental fusion technique upon which to construct the required framework. This is based both on the ability of the Kalman filter to flexibly accommodate well-established traffic flow models which describe the internal physical relation between the observed variables and objective estimates and on its ability to integrate and propagate in a consistent fashion the uncertainty associated with different data sources. Although the standard linear Kalman filter has been used for multi-sensor travel time estimation in the previous research, the novelty of this research is to develop a nonlinear Kalman filter (EKF and UKF) fusion framework which improves the estimation performance over those methods based on the linear Kalman filter. This proposed framework is validated by both of simulation and real-world scenarios, and is demonstrated the effectiveness of estimating travel time by fusing multi-sensor sources.

Fusion is a fundamental human process that occurs in some form at all levels of sense organs such as visual and sound information received from eyes and ears respectively, to the highest levels of decision making such as our brain fuses visual and sound information to make decisions. Multi-sensor data fusion is concerned with gaining information from multiple sensors by fusing across raw data, features or decisions. The traditional frameworks for multi-sensor data fusion only concern fusion at specific points in time. However, many real world situations change over time. When the multi-sensor system is used for situation awareness, it is useful not only to know the state or event of the situation at a point in time, but also more importantly, to understand the causalities of those states or events changing over time.Hence, we proposed a multi-agent framework for temporal fusion, which emphasises the time dimension of the fusion process, that is, fusion of the multi-sensor data or events derived over a period of time. The proposed multi-agent framework has three major layers: hardware, agents, and users. There are three different fusion architectures: centralized, hierarchical, and distributed, for organising the group of agents. The temporal fusion process of the proposed framework is elaborated by using the information graph. Finally, the core of the proposed temporal fusion framework – Dynamic Time Warping (DTW) temporal fusion agent is described in detail.Fusing multisensory data over a period of time is a challenging task, since the data to be fused consists of complex sequences that are multi–dimensional, multimodal, interacting, and time–varying in nature. Additionally, performing temporal fusion efficiently in real–time is another challenge due to the large amount of data to be fused. To address these issues, we proposed the DTW temporal fusion agent that includes four major modules: data pre-processing, DTW recogniser, class templates, and decision making. The DTW recogniser is extended in various ways to deal with the variability of multimodal sequences acquired from multiple heterogeneous sensors, the problems of unknown start and end points, multimodal sequences of the same class that hence has different lengths locally and/or globally, and the challenges of online temporal fusion.We evaluate the performance of the proposed DTW temporal fusion agent on two real world datasets: 1) accelerometer data acquired from performing two hand gestures, and 2) a benchmark dataset acquired from carrying a mobile device and performing pre-defined user scenarios. Performance results of the DTW based system are compared with those of a Hidden Markov Model (HMM) based system. The experimental results from both datasets demonstrate that the proposed DTW temporal fusion agent outperforms HMM based systems, and has the capability to perform online temporal fusion efficiently and accurately in real–time.