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Academic literature on the topic 'High gain unscented Kalman filter'

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Published: 25 May 2024

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Journal articles on the topic "High gain unscented Kalman filter"

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Yem Souhe, Felix Ghislain, Alexandre Teplaira Boum, Pierre Ele, Camille Franklin Mbey, and Vinny Junior Foba Kakeu. "A Novel Smart Method for State Estimation in a Smart Grid Using Smart Meter Data." Applied Computational Intelligence and Soft Computing 2022 (May 10, 2022): 1–14. http://dx.doi.org/10.1155/2022/7978263.

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Smart grids have brought new possibilities in power grid operations for control and monitoring. For this purpose, state estimation is considered as one of the effective techniques in the monitoring and analysis of smart grids. State estimation uses a processing algorithm based on data from smart meters. The major challenge for state estimation is to take into account this large volume of measurement data. In this article, a novel smart distribution network state estimation algorithm has been proposed. The proposed method is a combined high-gain state estimation algorithm named adaptive extended Kalman filter (AEKF) using extended Kalman filter (EKF) and unscented Kalman filter (UKF) in order to achieve better intelligent utility grid state estimation accuracy. The performance index and the error are indicators used to evaluate the accuracy of the estimation models in this article. An IEEE 37-node test network is used to implement the state estimation models. The state variables considered in this article are the voltage module at the measurement nodes. The results obtained show that the proposed hybrid algorithm has better performance compared to single state estimation methods such as the extended Kalman filter, the unscented Kalman filter, and the weighted least squares (WLS) method.
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Lv, Jiechao, Baochen Jiang, Xiaoli Wang, Yirong Liu, and Yucheng Fu. "Estimation of the State of Charge of Lithium Batteries Based on Adaptive Unscented Kalman Filter Algorithm." Electronics 9, no. 9 (September 2, 2020): 1425. http://dx.doi.org/10.3390/electronics9091425.

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The state of charge (SOC) estimation of the battery is one of the important functions of the battery management system of the electric vehicle, and the accurate SOC estimation is of great significance to the safe operation of the electric vehicle and the service life of the battery. Among the existing SOC estimation methods, the unscented Kalman filter (UKF) algorithm is widely used for SOC estimation due to its lossless transformation and high estimation accuracy. However, the traditional UKF algorithm is greatly affected by system noise and observation noise during SOC estimation. Therefore, we took the lithium cobalt oxide battery as the analysis object, and designed an adaptive unscented Kalman filter (AUKF) algorithm based on innovation and residuals to estimate SOC. Firstly, the second-order RC equivalent circuit model was established according to the physical characteristics of the battery, and the least square method was used to identify the parameters of the model and verify the model accuracy. Then, the AUKF algorithm was used for SOC estimation; the AUKF algorithm monitors the changes of innovation and residual in the filter and updates system noise covariance and observation noise covariance in real time using innovation and residual, so as to adjust the gain of the filter and realize the optimal estimation. Finally came the error comparison analysis of the estimation results of the UKF algorithm and AUKF algorithm; the results prove that the accuracy of the AUKF algorithm is 2.6% better than that of UKF algorithm.
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Riva, Mauro H., Matthias Dagen, and Tobias Ortmaier. "Adaptive High-Gain observer for joint state and parameter estimation: A comparison to Extended and Unscented Kalman filter." IFAC Proceedings Volumes 47, no. 3 (2014): 8558–63. http://dx.doi.org/10.3182/20140824-6-za-1003.01609.

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Bagheri, Ahmad, Shahram Azadi, and Abbas Soltani. "A combined use of adaptive sliding mode control and unscented Kalman filter estimator to improve vehicle yaw stability." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 231, no. 2 (October 7, 2016): 388–401. http://dx.doi.org/10.1177/1464419316673960.

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In this paper, an adaptive sliding mode controller is proposed to improve the vehicle yaw stability and enhance the lateral motion by direct yaw moment control method using active braking systems. As the longitudinal and lateral velocities of the vehicles as well as many other vehicle dynamics variables cannot be measured in a cost-efficient way, a robust control method combined with a state estimator is required to guarantee the system stability. Furthermore, some parameters such as the tyre–road friction coefficient undergo frequent changes, and the aerodynamics resistance forces are often exerted as a disturbance during the wide driving condition. So, an adaptive sliding mode controller is applied to make vehicle yaw rate to track its reference with robustness against model uncertainties and disturbances and a non-linear estimator based on unscented Kalman filter is used to estimate wheel slip, yaw rate, road friction coefficient, longitudinal and lateral velocities. The estimation algorithm directly uses non-linear equations of the system and does not need the linearization and differentiation. The designed controller, which is insensitive to system uncertainties, offers the adaptive sliding gains to eliminate the precise determination of the bounds of uncertainties. The sliding gain values are determined using a simple adaptation algorithm that does not require extensive computational load. Numerical simulations of various manoeuvres using a non-linear full vehicle model with seven degrees of freedom demonstrate the high effectiveness of the presented controller for improving the vehicle yaw stability and handling performance.
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Chen, Yudi, Xiangyu Liu, Changqing Li, Jiao Zhu, Min Wu, and Xiang Su. "UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering." Electronics 13, no. 6 (March 12, 2024): 1054. http://dx.doi.org/10.3390/electronics13061054.

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When an unmanned aerial vehicles (UAV) swarm is used for edge computing, and high-speed data transmission is required, accurate tracking of the UAV swarm’s centroid is of great significance for the acquisition and synchronization of signal demodulation. Accurate centroid tracking can also be applied to accurate communication beamforming and angle tracking, bringing about a reception gain. Group target tracking (GTT) offers a suitable framework for tracking the centroids of UAV swarms. GTT typically involves accurate modeling of target maneuvering behavior and effective state filtering. However, conventional coordinate-uncoupled maneuver models and multi-model filtering methods encounter difficulties in accurately tracking highly maneuverable UAVs. To address this, an innovative approach known as 3DCDM-based GRU-MM is introduced for tracking the maneuvering centroid of a UAV swarm. This method employs a multi-model filtering technique assisted by a gated recurrent unit (GRU) network based on a suitable 3D coordinate-coupled dynamic model. The proposed dynamic model represents the centroid’s tangential load, normal load, and roll angle as random processes, from which a nine-dimensional unscented Kalman filter is derived. A GRU is utilized to update the model weights of the multi-model filtering. Additionally, a smoothing-differencing module is presented to extract the maneuvering features from position observations affected by measurement noise. The resulting GRU-MM method achieved a classification accuracy of 99.73%, surpassing that of the traditional IMM algorithm based on the same model. Furthermore, our proposed 3DCDM-based GRU-MM method outperformed the Singer-KF and 3DCDM-based IMM-EKF in terms of the RMSE for position estimation, which provides a basis for further edge computing.
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Matsuura, Tsubasa, Masahiro Matsushita, Gan Chen, and Isao Takami. "Gain-scheduled Control Using Unscented Kalman Filter." Proceedings of Conference of Tokai Branch 2019.68 (2019): 316. http://dx.doi.org/10.1299/jsmetokai.2019.68.316.

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Gao, She Sheng, Wen Hui Wei, and Li Xue. "Near Space Pseudolite Navigation System Design and High-Performance Filtering Algorithm." Applied Mechanics and Materials 411-414 (September 2013): 931–35. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.931.

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This paper analyzes the defects of satellite navigation systems that exist in positioning and precision-guided weapons and pointes out the advantages and military needs of pseudolite. The autonomous navigation nonlinear mathematical model of Near Space Pseudolite SINS/CNS/SAR autonomous navigation system is established. Based on the merits of fading filter, robust adaptive filtering and particle filter, we propose a fading adaptive Unscented Particle Filtering algorithm. The proposed filtering algorithm is applied to SINS/CNS/SAR autonomous navigation system and conducted simulation calculation with the Unscented Kalman filter and particle filter comparison. The results show that the new algorithm that is proposed meets the needs of pseudolite autonomous navigation, and the navigation accuracy is significantly higher than the Unscented Kalman filter and particle filter algorithm.
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Xu, Daxing, Bao Wang, Lu Zhang, and Zhiqiang Chen. "A New Adaptive High-Degree Unscented Kalman Filter with Unknown Process Noise." Electronics 11, no. 12 (June 13, 2022): 1863. http://dx.doi.org/10.3390/electronics11121863.

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Vehicle state, including location and motion information, plays an essential role on the Internet of Vehicles (IoV). Accurately obtaining the system state information is the premise of realizing precise control. However, the statistics of system process noise are often unknown due to the complex physical process. It is challenging to estimate the system state when the process noise statistics are unknown. This paper proposes a new adaptive high-degree unscented Kalman filter based on the improved Sage–Husa algorithm. First, the traditional Sage–Husa algorithm is improved using a high-degree unscented transform. A noise estimator suitable for the high-degree unscented Kalman filter is obtained to estimate the statistics of the unknown process noise. Then, an adaptive high-degree unscented Kalman filter is designed to improve the accuracy and stability of the state estimation system. Finally, the target tracking simulation results verify the proposed algorithm’s effectiveness.
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Xi, Yan Hui, and Hui Peng. "Training Multi-Layer Perceptrons with the Unscented Kalman Particle Filter." Advanced Materials Research 542-543 (June 2012): 745–48. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.745.

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Many Bayesian learning approaches to multi-layer perceptrons (MLPs) parameters optimization have been proposed such as the extended Kalman filter (EKF). In this paper, a sequential approach is applied to train the MLPs. Based on the particle filter, the approach named unscented Kalman particle filter (UPF) uses the unscented Kalman filter as proposal distribution to generate the importance sampling density. The UPF are devised to deal with the high dimensional parameter space that is inherent to neural network models. Simulation results show that the new algorithm performs better than traditional optimization methods such as the extended Kalman filter.
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Li, Chengyi, and Chenglin Wen. "A Novel Extended Unscented Kalman Filter Is Designed Using the Higher-Order Statistical Property of the Approximate Error of the System Model." Actuators 13, no. 5 (May 1, 2024): 169. http://dx.doi.org/10.3390/act13050169.

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In the actual working environment, most equipment models present nonlinear characteristics. For nonlinear system filtering, filtering methods such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Cubature Kalman Filter (CKF) have been developed successively, all of which show good results. However, in the process of nonlinear system filtering, the performance of EKF decreases with an increase in the truncation error and even diverges. With improvement of the system dimension, the sampling points of UKF are relatively few and unrepresentative. In this paper, a novel high-order extended Unscented Kalman Filter (HUKF) based on an Unscented Kalman Filter is designed using the higher-order statistical properties of the approximate error. In addition, a method for calculating the approximate error of the multi-level approximation of the original function under the condition that the measurement is not rank-satisfied is proposed. The effectiveness of the filter is verified using digital simulation experiments.
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Dissertations / Theses on the topic "High gain unscented Kalman filter"

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Daid, Assia. "Sur la convergence d’unscented Kalman filter." Electronic Thesis or Diss., Toulon, 2021. http://www.theses.fr/2021TOUL0013.

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Notre travail consiste à étudier les propriétés du filtre unscented Kalman filter "UKF" et son adaptation en tant qu'observateur non linéaire. Il a été développé mais, bien que donnant en pratique de meilleurs résultats, aucune preuve de convergence n’existe qui garantit son utilisation.Un résultat négatif a été obtenu (sa non-convergence). Ce qui nous a conduit à proposer une nouvelle version : "unscented Kalman observer" pour les systèmes à temps continu dans un cadre déterministe. Nous avons montré sa convergence, lorsque les erreurs d'estimations initiales sont suffisamment petites. Cette démonstration repose essentiellement sur l'existence de bornes de la solution de l'équation de Riccati qui est perturbé par un terme non Linéaire.On a aussi développer une version grand gain de l'observateur UKF, qui est un compromis entre le filtre de Kalman étendu grand gain (EKF) et le unscented Kalman filter grand grand (HG-UKF). Toutes ces propriétés sont illustrées sur l'exemple de la colonne de distillation binaire et sur un exemple de géolocalisation
The present thesis is a study of the convergence of the unscented Kalman filter. A convergence analysis of the modified unscented Kalman filter ( used as an observer for a class of nonlinear deterministic continuous time systems, is presented. Under certain conditions, the extended Kalman filter ( is an exponential observer for non linear systems, i.e., the dynamics of the estimation error is exponentially stable. It is shown that unlike the EKF, the UKF is not an expo nentially converging observer. A modification of the UKF the unscented Kalman observer ( is proposed, which is a better candidate for an observer, we proved the exponentialconvergence of the UKO and also shown that the high gain UKF observer as a compromise between the high gain extended Kalman filter (HG EKF) and the high gain unscented Kalman filter (HG UKF). All these properties are illustrated on the example of the binary distillation column and on an example of geolocalization
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Boizot, Nicolas. "Adaptative high-gain extended Kalman filter and applications." Phd thesis, Université de Bourgogne, 2010. http://tel.archives-ouvertes.fr/tel-00559107.

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The work concerns the "observability problem"--the reconstruction of a dynamic process's full state from a partially measured state-- for nonlinear dynamic systems. The Extended Kalman Filter (EKF) is a widely-used observer for such nonlinear systems. However it suffers from a lack of theoretical justifications and displays poor performance when the estimated state is far from the real state, e.g. due to large perturbations, a poor initial state estimate, etc. . . We propose a solution to these problems, the Adaptive High-Gain (EKF). Observability theory reveals the existence of special representations characterizing nonlinear systems having the observability property. Such representations are called observability normal forms. A EKF variant based on the usage of a single scalar parameter, combined with an observability normal form, leads to an observer, the High-Gain EKF, with improved performance when the estimated state is far from the actual state. Its convergence for any initial estimated state is proven. Unfortunately, and contrary to the EKF, this latter observer is very sensitive to measurement noise. Our observer combines the behaviors of the EKF and of the high-gain EKF. Our aim is to take advantage of both efficiency with respect to noise smoothing and reactivity to large estimation errors. In order to achieve this, the parameter that is the heart of the high-gain technique is made adaptive. Voila, the Adaptive High-Gain EKF. A measure of the quality of the estimation is needed in order to drive the adaptation. We propose such an index and prove the relevance of its usage. We provide a proof of convergence for the resulting observer, and the final algorithm is demonstrated via both simulations and a real-time implementation. Finally, extensions to multiple output and to continuous-discrete systems are given.
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Akhtar, Jahanzeb. "Particle tracking using the unscented Kalman filter in high energy physics experiments." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/11482.

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The extended Kalman lter (EKF) has a long history in the field of non-linear tracking. More recently, statistically-based estimators have emerged that avoid the need for a deterministic linearisation process. The Unscented Kalman filter (UKF) is one such technique that has been shown to perform favourably for some non-linear systems when compared to an EKF implementation, both in terms of accuracy and robustness. In this Thesis, the UKF is applied to a high energy physics particle tracking problem where currently the EKF is being implemented. The effects of measurement redundancy are investigated to determine improvements in accuracy of particle track reconstruction. The relationship between measurement redundancy and relative observability is also investigated through an experimental and theoretical analysis. Smoothing (backward filtering), in the high energy physics experiments, is implementedusing the Rauch Tung Striebel (RTS) smoother with the EKF , however, in Unscented Kalman filter algorithms, the Jacobian matrices required by the RTS method, are not available. The Unscented Rauch Tung Striebel (URTS) smoother addresses this problem by avoiding the use of Jacobian matrices but is not effi cient for large dimensional systems such as high energy physics experiments. A technique is implemented in the RTS smoother to make it suitable for the UKF. The method is given the name the Jacobian Equivalent Rauch Tung Striebel (JE-RTS) smoother. The implementation of this method is quite straight forward when the UKF is used as an estimator.
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Paulsen, Trevor H. "Low cost/high precision flight dynamics estimation using the square-root unscented Kalman filter /." Diss., CLICK HERE for online access, 2010. http://contentdm.lib.byu.edu/ETD/image/etd3181.pdf.

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Paulsen, Trevor H. "Low Cost/ High Precision Flight Dynamics Estimation Using the Square-Root Unscented Kalman Filter." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1922.

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For over a decade, Brigham Young University's Microwave Earth Remote Sensing (MERS) team has been developing SAR systems and SAR processing algorithms. In order to create the most accurate image reconstruction algorithms, detailed aircraft motion data is essential. In 2008, the MERS team purchased a costly inertial measurement unit (IMU) coupled with a high precision global positioning system (GPS) from NovAtel, Inc. In order to lower the cost of obtaining detailed motion measurements, the MERS group decided to build a system that mimics the capability the NovAtel system as closely as possible for a much lower cost. As a first step, the same sensors and a simplified set of flight dynamics are used. This thesis presents a standalone motion sensor recording system (MOTRON), and outlines a method of utilizing the square-root Unscented Kalman filter (SR-UKF) to estimate aircraft flight dynamics, based on recorded flight data, as an alternative to the extended Kalman filter. While the results of the SR-UKF are not as precise as the NovAtel results, they approach the accuracy of the NovAtel system despite the simplified dynamics model.
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Feddaoui-Papin, Aïda. "Observateurs non linéaires pour les systèmes à mesures asynchrones : application robotique mobile." Electronic Thesis or Diss., Toulon, 2020. http://www.theses.fr/2020TOUL0008.

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L’étude de l’observabilité et la synthèse d’observateur ont pour vocation la reconstruction de l’état d’un système à l’aide des mesures reçues. Ces dernières n’apportent généralement qu’une connaissance partielle de cet état. Le filtre de Kalman est un observateur particulièrement étudié et employé. Plusieurs versions existent, adaptées aux systèmes linéaires ou non linéaires, en version discrète, continue voire continue-discrète, dans le cadre stochastique ou déterministe. Ces observateurs reposent cependant sur l’hypothèse que les mesures fournies par les capteurs sont synchrones. Or, cette supposition est assez éloignée de la réalité physique, notamment des problèmes étudiés en robotique.Nous proposons dans ce manuscrit un observateur adapté aux systèmes non linéaires continusdiscrets asynchrones. Nous entendons par cela des systèmes dont l’état est continu et les sorties échantillonnées à des fréquences différentes. En nous basant sur le Filtre de Kalman Etendu grand-gain existant pour les systèmes non linéaires continus et continus-discrets synchrones, nous développons un formalisme et construisons un observateur en adoptant un point de vue déterministe. Sa convergence est prouvée analytiquement et illustrée par une application sur un système robotique mobile
The aim of observability studies and observer design is to reconstruct the state of a dynamic system using the measurements available. In particular, the Kalman filter algorithm is considered. This widely-studied and used observer exists in several versions : for linear or nonlinear systems, for discrete, continuous or even continuous-discrete time, in the stochastic or deterministic framework. However, Most of the time, these observers are used with the assumption that the measurements provided by the sensors are synchronous. Most of the time, this assumption can be far from the physical reality, in particular when dealing with robotic systems. In this memoir, an observer tailored for nonlinear continuous-discrete asynchronous systems is presented. These systems are made of a continuous state equation and a multirate sampled output equation. Based on the existing high-gain Extended Kalman Filter for continuous nonlinear systems and continuous-discrete nonlinear systems with synchronous outputs, we develop an ad-hoc formalism and design an observer with a deterministic point of view. Its convergence is proven analytically and illustrated by an application on a mobile robotic system
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Book chapters on the topic "High gain unscented Kalman filter"

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Yerra, Yashwant, D. Ram Kumar Reddy, and P. Sudheesh. "An Unscented Kalman Filter Approach for High-Precision Indoor Localization." In Intelligent Manufacturing and Energy Sustainability, 433–41. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4443-3_42.

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Bianconi, Fortunato, Gabriele Lillacci, and Paolo Valigi. "Dynamic Modeling and Parameter Identification for Biological Networks." In Handbook of Research on Computational and Systems Biology, 478–510. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-491-2.ch021.

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Then, two different parameter identification techniques are presented for the proposed models. One is based on a least squares procedure, which treats the signals provided by a high gain observer; the other one is based on a Mixed Extended Kalman Filter. Prior to the estimation phase, identifiability and sensitivity analyses are used to determine which parameters can be and/or should be estimated. The procedures are tested and compared by means of data obtained by in silico experiments.
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Conference papers on the topic "High gain unscented Kalman filter"

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Ceresoli, Michele, Giovanni Zanotti, and Michèle Lavagna. "Leveraging Sensors Fusion to Enhance One-way Lunar Navigation Signals." In ESA 12th International Conference on Guidance Navigation and Control and 9th International Conference on Astrodynamics Tools and Techniques. ESA, 2023. http://dx.doi.org/10.5270/esa-gnc-icatt-2023-201.

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In recent years, the Moon has been identified as a key testing ground to develop and enhance technologies for future deep-space missions, resulting in an ever-growing number of planned Moon-targeted launches from both space agencies and commercial actors. Despite having different objectives, all these users share the need to maintain accurate and reliable state estimates. In this regard, The European Space Agency (ESA) has launched the Moonlight initiative to foster the development of a dedicated Lunar Communication and Navigation System (LCNS) exploiting a small satellite constellation in lunar orbits. LCNS will deliver cost-efficient and high-performance navigation and communication services to future lunar exploration missions, enabling users simplifying their design and lowering the size, mass and power demands of the navigation payload. Additionally, the deployment of a third-party constellation may facilitate the on-board implementation of autonomous lunar navigation systems, enabling rovers, landers and spacecraft to explore the lunar environment with no need for constant human control. At the beginning of the constellation setup, a key challenge will be the limited availability of navigation signals, due to the significantly smaller number of LCNS servicing satellites with respect to well-established Earth Global Navigation Satellite Systems (GNSS). In particular, the initial architecture will focus on maximizing the navigation performance of surface and Low Lunar Orbital (LLO) users at latitudes around the South Pole region, which is the subject of great scientific interest. Nevertheless, even for South Pole users, the LCNS constellation will still be too small to provide a continuous view of at least 4 satellites at any location, requiring the development of tailored navigation algorithms to cope with the mission requirements. To overcome these limitations, the paper investigates strategies to mitigate the impact of these reduced-servicers-visibility windows on the estimation process by exploiting sensor fusion techniques to integrate the one-way ranging LCNS signals with a suite of well-known sensors, which might include Inertial Measurement Units (IMUs), optical cameras, altimeters and Two-Way Ranging (TWR) with the Lunar Gateway (LOP-G). The observability gain provided by each sensor is analyzed as a function of the relative servicer-user dynamics, the required pointing strategy and the measurements availability. Different inclinations of LLO are analyzed to investigate the most favorable LCNS receiver antenna pointing direction that maximizes the overall number of visible satellites throughout the simulation window. Additionally, sensors mounting location on-board the spacecraft is discussed in relationship to the LLO inclination and satellite attitude, to identify possible operational constraints that may influence the observables availability and to identify the best suited sensors clustering for a given mission profile and operational needs. The simulation architecture consists of a high-fidelity non-Keplerian dynamics propagator, which accounts for irregular lunar gravity field and Solar Radiation Pressure (SRP) perturbations; a sensor suite to have a truthful noisy representation of the available spacecraft measurements; a dedicated sequential navigation filter. Each proposed navigation scheme underwent a Montecarlo testing campaign to comprehensively fix and tune the algorithm gains and parameters. The sensitivity of the navigation solution on the measurement noise has been assessed for each type of observable, allowing for a direct link between commercial sensor properties and the resulting navigation errors and uncertainties. A tightly coupled approach is exploited to directly fuse the LCNS one-way ranging measurements with other sensors data, making the observables availability hold irrespective of the actual number of visible LCNS satellites. The filter is constructed to estimate both the spacecraft state and the receiver clock bias and drift, allowing to properly account for the de-synchronization of the generally low-cost user clocks with respect to the high precision atomic clocks on-board of the LCNS constellation. The outcomes show that with the sole LCNS signals and a proper pointing strategy exploitation, the navigation error swings between 10 meters, with 4 visible satellites, and 1 km when the user is above the lunar North Pole and the LCNS servicers are obstructed by the Moon. Each of the analyzed sensors further improves these statistics: for low altitude LLO users, an altimeter provides continuous discrete altitude measurements that can improve the overall error up to 60%, especially if long LCNS-unavailability windows are present. On the other hand, although TWR with LOP-G effectively offers an additional high precision observable, its availability is often synchronized with that of the LCNS satellites: being on a Southern Near Rectilinear Halo Orbit (S-NRHO), the Gateway can only provide measurements for users located above the Lunar North Pole only for a day per week. Additionally, the performance of a variety of Runge-Kutta (RK) explicit integration schemes and dynamic approximations is tested for both Extended Kalman Filters (EKF) and Unscented Kalman Filters (UKF). A trade-off between solution accuracy and computation burden (i.e., number of required dynamic function evaluations) is performed to accommodate the needs of different spacecraft classes and available hardware. The results highlight that for filters running at 1 Hz a two-stage Heun integration method halves the CPU time required for the computations while bounding the propagation error difference with respect to Verner’s “Most Efficient” 8(7) scheme to less than 5 meters. On the other hand, at lower filter frequencies (e.g., 1/60 Hz), the same difference grows up to hundreds of meters, thus requiring a more accurate integration to properly navigate. The paper exhaustively presents that with the proper navigation sensors, autonomous lunar navigation exploiting a dedicated Moon-centered GNSS-like infrastructure can provide accurate and reliable state estimates, paving the way to a new generation of lunar exploration missions with enhanced autonomy and reliability. The present study has been carried out in collaboration with Telespazio.
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Bucci, Alessandro, Alessandro Ridolfi, Matteo Franchi, and Benedetto Allotta. "Covariance and Gain-based Federated Unscented Kalman Filter for Acoustic-Visual-Inertial Underwater Navigation." In OCEANS 2021: San Diego – Porto. IEEE, 2021. http://dx.doi.org/10.23919/oceans44145.2021.9705843.

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Zhang, Limin, Zengqiang Chen, and Xinghui Zhang. "A novel varible gain unscented kalman filter and its application in the integrated navigation system." In 2012 10th World Congress on Intelligent Control and Automation (WCICA 2012). IEEE, 2012. http://dx.doi.org/10.1109/wcica.2012.6358056.

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Zhang, Xinming, Bo Yang, Shan Li, and Aidong Men. "An Unscented Kalman Filter for ICI Cancellation in High-Mobility OFDM System." In 2011 IEEE Vehicular Technology Conference (VTC 2011-Spring). IEEE, 2011. http://dx.doi.org/10.1109/vetecs.2011.5956315.

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Yin, Haohao, Weiwei Xia, Yueyue Zhang, and Lianfeng Shen. "UWB-based indoor high precision localization system with robust unscented Kalman filter." In 2016 IEEE International Conference on Communication Systems (ICCS). IEEE, 2016. http://dx.doi.org/10.1109/iccs.2016.7833646.

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Shen, Kelei, Hongyu Ni, Qiang Lu, Jidong Cai, and Wenxu Yan. "Power System State Estimation Based on Improved Strong Tracking Unscented Kalman Filter." In 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE). IEEE, 2022. http://dx.doi.org/10.1109/ichve53725.2022.9961457.

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Wang, Wen-jing, Xi Chen, Shuai Han, Wei-xiao Meng, and Yi Zhang. "Unscented Kalman filter with open-loop compensation for high dynamic GNSS carrier tracking." In International Conference on Space Information Technology 2009, edited by Xingrui Ma, Baohua Yang, and Ming Li. SPIE, 2009. http://dx.doi.org/10.1117/12.855176.

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Xu, Longyuan, Peng Tong, and Yinsheng Wei. "Sequential Multi-model Unscented Kalman Filter for Shipborne High Frequency Surface Wave Radar." In 2023 IEEE International Radar Conference (RADAR). IEEE, 2023. http://dx.doi.org/10.1109/radar54928.2023.10371192.

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9

Bu, Xiangyuan, Weiping Zeng, and Yanbo Wu. "High Dynamic Pseudo-Random Code Tracking Using Unscented Kalman Filter and Carrier-Aiding Technology." In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2008. http://dx.doi.org/10.1109/wicom.2008.478.

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10

Lu, Peng, Timothy Sandy, and Jonas Buchli. "Adaptive Unscented Kalman Filter-based Disturbance Rejection With Application to High Precision Hydraulic Robotic Control." In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8970476.

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