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Relevant bibliographies by topics / Data fusion algorithms; Information filters

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Academic literature on the topic 'Data fusion algorithms; Information filters'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Data fusion algorithms; Information filters"

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Jahan, Kausar, and Koteswara Rao Sanagapallea. "Fusion of Angle Measurements from Hull Mounted and Towed Array Sensors." Information 11, no. 9 (September 9, 2020): 432. http://dx.doi.org/10.3390/info11090432.

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Two sensor arrays, hull-mounted array, and towed array sensors are considered for bearings-only tracking. An algorithm is designed to combine the information obtained as bearing (angle) measurements from both sensor arrays to give a better solution. Using data from two different sensor arrays reduces the problem of observability and the observer need not follow the S-maneuver to attain observability of the process. The performance of the fusion algorithm is comparable to that of theoretical Cramer–Rao lower bound and with that of the algorithm when bearing measurements from a single sensor array are considered. Different filters are used for analyzing both algorithms. Monte Carlo runs need to be done to evaluate the performance of algorithms more accurately. Also, the performance of the fusion algorithm is evaluated in terms of solution convergence time.

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HASAN, AHMED M., KHAIRULMIZAM SAMSUDIN, ABDUL RAHMAN RAMLI, and RAJA SYAMSUL AZMIR. "COMPARATIVE STUDY ON WAVELET FILTER AND THRESHOLDING SELECTION FOR GPS/INS DATA FUSION." International Journal of Wavelets, Multiresolution and Information Processing 08, no. 03 (May 2010): 457–73. http://dx.doi.org/10.1142/s0219691310003572.

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Navigation and guidance of an autonomous vehicle require determination of the position and velocity of the vehicle. Therefore, fusing the Inertial Navigation System (INS) and Global Positioning System (GPS) is important. Various methods have been applied to smooth and predict the INS and GPS errors. Recently, wavelet de-noising methodologies have been applied to improve the accuracy and reliability of the GPS/INS system. In this work, analysis of real data to identify the optimal wavelet filter for each GPS and INS component for high quality error estimation is presented. A comprehensive comparison of various wavelet thresholding selections with different level of decomposition is conducted to study the effect on GPS/INS error estimation while maintaining the original features of the signal. Results show that while some wavelet filters and thresholding selection algorithms perform better than others on each of the GPS and INS components, no specific parameter selection perform uniformly better than others.

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Guoyan, Wang, A. V. Fomichev, and Dy Yiran. "Research on Improved Gaussian Smoothing Filters for SLAM Application." Mekhatronika, Avtomatizatsiya, Upravlenie 20, no. 12 (December 6, 2019): 756–64. http://dx.doi.org/10.17587/mau.20.756-764.

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To address the navigation issues of the planetary rover and construct a map for the unknown environment as well as the surface of the planets in our solar system, the simultaneous localization and mapping can be seen as an alternative method. In terms of the navigation with the laser sensor, the Kalman filter and its improving algorithms, such as EKF and UKF are widely used in the the process of processing information. Nevertheless, these filter algorithms suffer from low accuracy and significant computation expensive. The EKF algorithm has a linearization process, the UKF algorithm is better matched in a nonlinear system than the EKF algorithm, but it has more computational complexity. The GP-RTSS filtering algorithm, based on a Gaussian filter, is significantly superior to the EKF and UKF algorithms regarding the sensor fusion accuracy. The Gaussian Process can be used in different non-linear system, does not need prediction model and linearization. However, the main barrier in the process of implementing the GP-RTSS algorithm is that the Gaussian core function requires a lot of computation. In this paper, an algorithm, so-called DIS RTSS filter under a distributed computation scheme, derived from the GP-RTSS Gaussia n smoothing and filter, is proposed. The distributed system can effectively reduce the cost of computation (computation expense and memory). Moreover, four fusion methods for the DIS RTSS filter, i.e., DIS RTP, DIS RTGP, DIS RTB, DIS RTrB are discussed in this paper. The experiments show that among the four algorithms described above, the DIS RTGP algorithm is the most effective solution for practical implementation. The DIS RTSS filtering algorithm can realize a high processing rate and can theoretically process an infinite number of data samples.

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López-Delis, Alberto, Cristiano J. Miosso, João L. A. Carvalho, Adson F. da Rocha, and Geovany A. Borges. "Continuous Estimation Prediction of Knee Joint Angles Using Fusion of Electromyographic and Inertial Sensors for Active Transfemoral Leg Prostheses." Advances in Data Science and Adaptive Analysis 10, no. 02 (April 2018): 1840008. http://dx.doi.org/10.1142/s2424922x18400089.

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Information extracted from the surface electromyographic (sEMG) signals can allow for the detection of movement intention in transfemoral prostheses. The sEMG can help estimate the angle between the femur and the tibia in the sagittal plane. However, algorithms based exclusively on sEMG information can lead to inaccurate results. Data captured by inertial-sensors can improve this estimate. We propose three myoelectric algorithms that extract data from sEMG and inertial sensors using Kalman-filters. The proposed fusion-based algorithms showed improved performance compared to methods based exclusively on sEMG data, generating improvements in the accuracy of knee joint angle estimation and reducing estimation artifacts.

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Alshawabkeh, Yahya. "Color and Laser Data as a Complementary Approach for Heritage Documentation." Remote Sensing 12, no. 20 (October 21, 2020): 3465. http://dx.doi.org/10.3390/rs12203465.

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Heritage recording has received much attention and benefits from recent developments in the field of range and imaging sensors. While these methods have often been viewed as two different methodologies, data integration can achieve different products, which are not always found in a single technique. Data integration in this paper can be divided into two levels: laser scanner data aided by photogrammetry and photogrammetry aided by scanner data. At the first level, superior radiometric information, mobility and accessibility of imagery can be actively used to add texture information and allow for new possibilities in terms of data interpretation and completeness of complex site documentation. In the second level, true orthophoto is generated based on laser data, the results are rectified images with a uniform scale representing all objects at their planimetric position. The proposed approaches enable flexible data fusion and allow images to be taken at an optimum time and position for radiometric information. Data fusion usually involves serious distortions in the form of a double mapping of occluded objects that affect the product quality. In order to enhance the efficiency of visibility analysis in complex structures, a proposed visibility algorithm is implemented into the developed methods of texture mapping and true orthophoto generation. The algorithm filters occluded areas based on a patch processing using a grid square unit set around the projected vertices. The depth of the mapped triangular vertices within the patch neighborhood is calculated to assign the visible one. In this contribution, experimental results from different historical sites in Jordan are presented as a validation of the proposed algorithms. Algorithms show satisfactory performance in terms of completeness and correctness of occlusion detection and spectral information mapping. The results indicate that hybrid methods could be used efficiently in the representation of heritage structures.

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Rao, Jin Jun, Tong Yue Gao, Zhen Jiang, and Zhen Bang Gong. "Position and Attitude Information Fusion for Portable Unmanned Aerial Vehicles." Key Engineering Materials 439-440 (June 2010): 155–60. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.155.

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Portable Unmanned Aerial Vehicles (PUAVs) present an enormous application potential, and the real time accurate position and attitude information is the basis of autonomous flight of PUAVs. In order to obtain comprehensive and accurate position and attitude data of PUAVs in flight, focusing on the common sensors configuration of PUAVs, each type of sensor’s characteristic is analyzed, and the data fusion problem of SINS/GPS/Compass combination is presented and studied in this paper. Firstly, the error expressions of MEMS inertia sensors, attitude, velocity and position are researched and derived, and the state equation and observation equation are built, and the discrete equations are derived for computer implementation, so the data fusion model for Kalman Filter fusion algorithms is presented. Then, the data fusion system and algorithms are implemented in software, and the flight data obtained in flight experiment is fed to the software for data fusion. The comparison between original data and fusional data shows that SINS/GPS/Compass data fusion system can promote accuracy of position and attitude markedly.

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Guan, Binglei, and Xianfeng Tang. "Multisensor decentralized nonlinear fusion using adaptive cubature information filter." PLOS ONE 15, no. 11 (November 5, 2020): e0241517. http://dx.doi.org/10.1371/journal.pone.0241517.

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In nonlinear multisensor system, abrupt state changes and unknown variance of measurement noise are very common, which challenges the majority of the previously developed models for precisely known multisensor fusion techniques. In terms of this issue, an adaptive cubature information filter (CIF) is proposed by embedding strong tracking filter (STF) and variational Bayesian (VB) method, and it is extended to multi-sensor fusion under the decentralized fusion framework with feedback. Specifically, the new algorithms use an equivalent description of STF, which avoid the problem of solving Jacobian matrix during determining strong trace fading factor and solve the interdependent problem of combination of STF and VB. Meanwhile, A simple and efficient method for evaluating global fading factor is developed by introducing a parameter variable named fading vector. The analysis shows that compared with the traditional information filter, this filter can effectively reduce the data transmission from the local sensor to the fusion center and decrease the computational burden of the fusion center. Therefore, it can quickly return to the normal error range and has higher estimation accuracy in response to abrupt state changes. Finally, the performance of the developed algorithms is evaluated through a target tracking problem.

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Wang, Tao, Xiaoran Wang, and Mingyu Hong. "Gas Leak Location Detection Based on Data Fusion with Time Difference of Arrival and Energy Decay Using an Ultrasonic Sensor Array." Sensors 18, no. 9 (September 7, 2018): 2985. http://dx.doi.org/10.3390/s18092985.

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Ultrasonic gas leak location technology is based on the detection of ultrasonic waves generated by the ejection of pressured gas from leak holes in sealed containers or pipes. To obtain more accurate leak location information and determine the locations of leak holes in three-dimensional space, this paper proposes an ultrasonic leak location approach based on multi-algorithm data fusion. With the help of a planar ultrasonic sensor array, the eigenvectors of two individual algorithms, i.e., the arrival distance difference, as determined from the time difference of arrival (TDOA) location algorithm, and the ratio of arrival distances from the energy decay (ED) location algorithm, are extracted and fused to calculate the three-dimensional coordinates of leak holes. The fusion is based on an extended Kalman filter, in which the results of the individual algorithms are seen as observation values. The final system state matrix is composed of distances between the measured leak hole and the sensors. Our experiments show that, under the condition in which the pressure in the measured container is 100 kPa, and the leak hole–sensor distance is 800 mm, the maximum error of the calculated results based on the data fusion location algorithm is less than 20 mm, and the combined accuracy is better than those of the individual location algorithms.

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Ahrari, A. H., M. Kiavarz, M. Hasanlou, and M. Marofi. "THERMAL AND VISIBLE SATELLITE IMAGE FUSION USING WAVELET IN REMOTE SENSING AND SATELLITE IMAGE PROCESSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 26, 2017): 11–15. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-11-2017.

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Multimodal remote sensing approach is based on merging different data in different portions of electromagnetic radiation that improves the accuracy in satellite image processing and interpretations. Remote Sensing Visible and thermal infrared bands independently contain valuable spatial and spectral information. Visible bands make enough information spatially and thermal makes more different radiometric and spectral information than visible. However low spatial resolution is the most important limitation in thermal infrared bands. Using satellite image fusion, it is possible to merge them as a single thermal image that contains high spectral and spatial information at the same time. The aim of this study is a performance assessment of thermal and visible image fusion quantitatively and qualitatively with wavelet transform and different filters. In this research, wavelet algorithm (Haar) and different decomposition filters (mean.linear,ma,min and rand) for thermal and panchromatic bands of Landast8 Satellite were applied as shortwave and longwave fusion method . Finally, quality assessment has been done with quantitative and qualitative approaches. Quantitative parameters such as Entropy, Standard Deviation, Cross Correlation, Q Factor and Mutual Information were used. For thermal and visible image fusion accuracy assessment, all parameters (quantitative and qualitative) must be analysed with respect to each other. Among all relevant statistical factors, correlation has the most meaningful result and similarity to the qualitative assessment. Results showed that mean and linear filters make better fused images against the other filters in Haar algorithm. Linear and mean filters have same performance and there is not any difference between their qualitative and quantitative results.

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Soundy, Andy W. R., Bradley J. Panckhurst, Phillip Brown, Andrew Martin, Timothy C. A. Molteno, and Daniel Schumayer. "Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data." Sensors 20, no. 21 (October 24, 2020): 6050. http://dx.doi.org/10.3390/s20216050.

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We recorded the time series of location data from stationary, single-frequency (L1) GPS positioning systems at a variety of geographic locations. The empirical autocorrelation function of these data shows significant temporal correlations. The Gaussian white noise model, widely used in sensor-fusion algorithms, does not account for the observed autocorrelations and has an artificially large variance. Noise-model analysis—using Akaike’s Information Criterion—favours alternative models, such as an Ornstein–Uhlenbeck or an autoregressive process. We suggest that incorporating a suitable enhanced noise model into applications (e.g., Kalman Filters) that rely on GPS position estimates will improve performance. This provides an alternative to explicitly modelling possible sources of correlation (e.g., multipath, shadowing, or other second-order physical phenomena).

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Dissertations / Theses on the topic "Data fusion algorithms; Information filters"

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Ho, Peter. "Organization in decentralized sensing." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306873.

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Baravdish, Ninos. "Information Fusion of Data-Driven Engine Fault Classification from Multiple Algorithms." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176508.

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As the automotive industry constantly makes technological progress, higher demands are placed on safety, environmentally friendly and durability. Modern vehicles are headed towards increasingly complex system, in terms of both hardware and software making it important to detect faults in any of the components. Monitoring the engine’s health has traditionally been done using expert knowledge and model-based techniques, where derived models of the system’s nominal state are used to detect any deviations. However, due to increased complexity of the system this approach faces limitations regarding time and knowledge to describe the engine’s states. An alternative approach is therefore data-driven methods which instead are based on historical data measured from different operating points that are used to draw conclusion about engine’s present state. In this thesis a proposed diagnostic framework is presented, consisting of a systematically approach for fault classification of known and unknown faults along with a fault size estimation. The basis for this lies in using principal component analysis to find the fault vector for each fault class and decouple one fault at the time, thus creating different subspaces. Importantly, this work investigates the efficiency of taking multiple classifiers into account in the decision making from a performance perspective. Aggregating multiple classifiers is done solving a quadratic optimization problem. To evaluate the performance, a comparison with a random forest classifier has been made. Evaluation with challenging test data show promising results where the algorithm relates well to the performance of random forest classifier.

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Lian, Chunfeng. "Information fusion and decision-making using belief functions : application to therapeutic monitoring of cancer." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2333/document.

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La radiothérapie est une des méthodes principales utilisée dans le traitement thérapeutique des tumeurs malignes. Pour améliorer son efficacité, deux problèmes essentiels doivent être soigneusement traités : la prédication fiable des résultats thérapeutiques et la segmentation précise des volumes tumoraux. La tomographie d’émission de positrons au traceur Fluoro- 18-déoxy-glucose (FDG-TEP) peut fournir de manière non invasive des informations significatives sur les activités fonctionnelles des cellules tumorales. Les objectifs de cette thèse sont de proposer: 1) des systèmes fiables pour prédire les résultats du traitement contre le cancer en utilisant principalement des caractéristiques extraites des images FDG-TEP; 2) des algorithmes automatiques pour la segmentation de tumeurs de manière précise en TEP et TEP-TDM. La théorie des fonctions de croyance est choisie dans notre étude pour modéliser et raisonner des connaissances incertaines et imprécises pour des images TEP qui sont bruitées et floues. Dans le cadre des fonctions de croyance, nous proposons une méthode de sélection de caractéristiques de manière parcimonieuse et une méthode d’apprentissage de métriques permettant de rendre les classes bien séparées dans l’espace caractéristique afin d’améliorer la précision de classification du classificateur EK-NN. Basées sur ces deux études théoriques, un système robuste de prédiction est proposé, dans lequel le problème d’apprentissage pour des données de petite taille et déséquilibrées est traité de manière efficace. Pour segmenter automatiquement les tumeurs en TEP, une méthode 3-D non supervisée basée sur le regroupement évidentiel (evidential clustering) et l’information spatiale est proposée. Cette méthode de segmentation mono-modalité est ensuite étendue à la co-segmentation dans des images TEP-TDM, en considérant que ces deux modalités distinctes contiennent des informations complémentaires pour améliorer la précision. Toutes les méthodes proposées ont été testées sur des données cliniques, montrant leurs meilleures performances par rapport aux méthodes de l’état de l’art
Radiation therapy is one of the most principal options used in the treatment of malignant tumors. To enhance its effectiveness, two critical issues should be carefully dealt with, i.e., reliably predicting therapy outcomes to adapt undergoing treatment planning for individual patients, and accurately segmenting tumor volumes to maximize radiation delivery in tumor tissues while minimize side effects in adjacent organs at risk. Positron emission tomography with radioactive tracer fluorine-18 fluorodeoxyglucose (FDG-PET) can noninvasively provide significant information of the functional activities of tumor cells. In this thesis, the goal of our study consists of two parts: 1) to propose reliable therapy outcome prediction system using primarily features extracted from FDG-PET images; 2) to propose automatic and accurate algorithms for tumor segmentation in PET and PET-CT images. The theory of belief functions is adopted in our study to model and reason with uncertain and imprecise knowledge quantified from noisy and blurring PET images. In the framework of belief functions, a sparse feature selection method and a low-rank metric learning method are proposed to improve the classification accuracy of the evidential K-nearest neighbor classifier learnt by high-dimensional data that contain unreliable features. Based on the above two theoretical studies, a robust prediction system is then proposed, in which the small-sized and imbalanced nature of clinical data is effectively tackled. To automatically delineate tumors in PET images, an unsupervised 3-D segmentation based on evidential clustering using the theory of belief functions and spatial information is proposed. This mono-modality segmentation method is then extended to co-segment tumor in PET-CT images, considering that these two distinct modalities contain complementary information to further improve the accuracy. All proposed methods have been performed on clinical data, giving better results comparing to the state of the art ones

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Jesneck, JL, LW Nolte, JA Baker, CE Floyd, and JY Lo. "Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis." Thesis, 2006. http://hdl.handle.net/10161/207.

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As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.
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Books on the topic "Data fusion algorithms; Information filters"

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Braun, Jerome J. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2011 : 27-28 April 2011, Orlando, Florida, United States. Bellingham, Wash: SPIE, 2011.

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Braun, Jerome J. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2010 : 7-8 April 2010, Orlando, Florida, United States. Bellingham, Wash: SPIE, 2010.

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(Society), SPIE, ed. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2009 : 16-17 April 2009, Orlando, Florida, United States. Bellingham, Wash: SPIE, 2009.

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K, Wang R., ed. Frequency domain filtering strategies for hybrid optical information processing. Taunton, Somerset, England: Research Studies Press, 1996.

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V, Dasarathy Belur, Society of Photo-optical Instrumentation Engineers., and Ball Aerospace & Technologies Corporation (USA), eds. Multisensor, multisource information fusion : architectures, algorithms, and applications 2005: 30-31 March, 2005, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2005.

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Multisensor, multisource information fusion: Architectures, algorithms, and applications 2004 : 14-15 April 2004, Orlando, Florida, USA. Bellingham, WA: SPIE, 2004.

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V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Multisensor, multisource information fusion--architectures, algorithms, and applications 2004: 14-15 April 2004, Orlando, Florida, USA. Bellingham, Wash., USA: SPIE, 2004.

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V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Multisensor, multisource information fusion: Architectures, algorithms, and applications 2006 : 19-20 April 2006, Kissimmee, Florida, USA. Bellingham, Wash: SPIE, 2006.

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V, Dasarathy Belur, and Society of Photo-optical Instrumentation Engineers., eds. Multisensor, multisource information fusion--architectures, algorithms, and applications 2003: 23-25 April 2003, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2003.

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Multisensor, multisource information fusion: Architectures, algorithms, and applications 2007 : 11-12 April, 2007, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2007.

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Book chapters on the topic "Data fusion algorithms; Information filters"

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Beltz, Hayley, Timothy Rutledge, Raoul R. Wadhwa, Péter Bruck, Jan Tobochnik, Anikó Fülöp, György Fenyvesi, and Péter Érdi. "Ranking Algorithms: Application for Patent Citation Network." In Information Fusion and Data Science, 519–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03643-0_21.

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Clark, James J., and Alan L. Yuille. "Data Fusion in Shape From Shading Algorithms." In Data Fusion for Sensory Information Processing Systems, 147–80. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4757-2076-1_7.

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Clark, James J., and Alan L. Yuille. "Data Fusion Applied to Feature Based Stereo Algorithms." In Data Fusion for Sensory Information Processing Systems, 105–35. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4757-2076-1_5.

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Chen, Dewang, and Ruijun Cheng. "Multiple GPS Track Information Fusion." In Intelligent Processing Algorithms and Applications for GPS Positioning Data of Qinghai-Tibet Railway, 117–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58970-0_7.

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Brighton, Henry, and Chris Mellish. "On the Consistency of Information Filters for Lazy Learning Algorithms." In Principles of Data Mining and Knowledge Discovery, 283–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-540-48247-5_31.

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Li, Chengfan, Jingyuan Yin, Junjuan Zhao, and Lan Liu. "Extraction of Urban Built-Up Land in Remote Sensing Images Based on Multi-sensor Data Fusion Algorithms." In Communications in Computer and Information Science, 243–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18129-0_39.

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Arasaratnam, Ienkaran, and Kumar Pakki Bharani Chandra. "Cubature Information Filters." In Multisensor Data Fusion, 193–206. CRC Press, 2017. http://dx.doi.org/10.1201/b18851-12.

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Gao, Wei, Ya Zhang, and Qian Sun. "Nonlinear Information Fusion Algorithm of an Asynchronous Multisensor Based on the Cubature Kalman Filter." In Multisensor Data Fusion, 223–33. CRC Press, 2017. http://dx.doi.org/10.1201/b18851-14.

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Meng, Tao, Mei-Ling Shyu, and Lin Lin. "Multimodal Information Integration and Fusion for Histology Image Classification." In Multimedia Data Engineering Applications and Processing, 35–50. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2940-0.ch003.

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Biomedical imaging technology has become an important tool for medical research and clinical practice. A large amount of imaging data is generated and collected every day. Managing and analyzing these data sets require the corresponding development of the computer based algorithms for automatic processing. Histology image classification is one of the important tasks in the bio-image informatics field and has broad applications in phenotype description and disease diagnosis. This study proposes a novel framework of histology image classification. The original images are first divided into several blocks and a set of visual features is extracted for each block. An array of C-RSPM (Collateral Representative Subspace Projection Modeling) models is then built that each model is based on one block from the same location in original images. Finally, the C-Value Enhanced Majority Voting (CEWMV) algorithm is developed to derive the final classification label for each testing image. To evaluate this framework, the authors compare its performance with several well-known classifiers using the benchmark data available from IICBU data repository. The results demonstrate that this framework achieves promising performance and performs significantly better than other classifiers in the comparison.

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Gharbia, Reham, and Aboul Ella Hassanien. "Swarm Intelligence Based on Remote Sensing Image Fusion." In Environmental Information Systems, 211–31. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7033-2.ch011.

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This chapter presents a remote sensing image fusion based on swarm intelligence. Image fusion is combining multi-sensor images in a single image that has most informative. Remote sensing image fusion is an effective way to extract a large volume of data from multisource images. However, traditional image fusion approaches cannot meet the requirements of applications because they can lose spatial information or distort spectral characteristics. The core of the image fusion is image fusion rules. The main challenge is getting suitable weight of fusion rule. This chapter proposes swarm intelligence to optimize the image fusion rule. Swarm intelligence algorithms are a family of global optimizers inspired by swarm phenomena in nature and have shown better performance. In this chapter, two remote sensing image fusion based on swarm intelligence algorithms, Particle Swarm Optimization (PSO) and flower pollination algorithm are presented to get an adaptive image fusion rule and comparative between them.

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Conference papers on the topic "Data fusion algorithms; Information filters"

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Roussel, Stephane, Hemanth Porumamilla, Charles Birdsong, Peter Schuster, and Christopher Clark. "Enhanced Vehicle Identification Utilizing Sensor Fusion and Statistical Algorithms." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12012.

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Several studies in the area of vehicle detection and identification involve the use of probabilistic analysis and sensor fusion. While several sensors utilized for identifying vehicle presence and proximity have been researched, their effectiveness in identifying vehicle types has remained inadequate. This study presents the utilization of an ultrasonic sensor coupled with a magnetic sensor and the development of statistical algorithms to overcome this limitation. Mathematical models of both the ultrasonic and magnetic sensors were constructed to first understand the intrinsic characteristics of the individual sensors and also to provide a means of simulating the performance of the combined sensor system and to facilitate algorithm development. Preliminary algorithms that utilized this sensor fusion were developed to make inferences relating to vehicle proximity as well as type. It was noticed that while it helped alleviate the limitations of the individual sensors, the algorithm was affected by high occurrences of false positives. Also, since sensors carry only partial information about the surrounding environment and their measured quantities are partially corrupted with noise, probabilistic techniques were employed to extend the preliminary algorithms to include these sensor characteristics. These statistical techniques were utilized to reconstruct partial state information provided by the sensors and to also filter noisy measurement data. This probabilistic approach helped to effectively utilize the advantages of sensor fusion to further enhance the reliability of inferences made on vehicle identification. In summary, the study investigated the enhancement of vehicle identification through the use of sensor fusion and statistical techniques. The algorithms developed showed encouraging results in alleviating the occurrences of false positive inferences. One of the several applications of this study is in the use of ultrasonic-magnetic sensor combination for advanced traffic monitoring such as smart toll booths.

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Lloyd, George M. "A Kalman Filter Framework for High-Dimensional Sensor Fusion Using Stochastic Non-Linear Networks." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-37834.

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The textbook Kalman Filter (LKF) seeks to estimate the state of a linear system based on having two things in hand: a.) a reasonable state-space model of the underlying process and its noise components; b.) imperfect (noisy) measurements obtained from the process via one or more sensors. The LKF approach results in a predictor-corrector algorithm which can be applied recursively to correct predictions from the state model so as to yield posterior estimates of the current process state, as new sensor data are made available. The LKF can be shown to be optimal in a Gaussian setting and is eminently useful in practical settings when the models and measurements are stochastic and non-stationary. Numerous extensions of the KF filter have been proposed for the non-linear problem, such as extended Kalman Filters (EKF) and ‘ensemble’ filters (EnKF). Proofs of optimality are difficult to obtain but for many problems where the ‘physics’ is of modest complexity EKF’s yield algorithms which function well in a practical sense; the EnKF also shows promise but is limited by the requirement for sampling the random processes. In multi-physics systems, for example, several complications arise, even beyond non-Gaussianity. On the one hand, multi-physics effects may include multi-scale responses and path dependency, which may be poorly sampled by a sensor suite (tending to favor low gains). One the other hand, as more multi-physics effects are incorporated into a model, the model itself becomes a less and less perfect model of reality (tending to favor high gains). For reasons such as these suitable estimates of the joint system response are difficult to obtain, as are corresponding joint estimates of the sensor ensemble. This paper will address these issues in a two-fold way — first by a generalized process model representation based on regularized stochastic non-linear networks (Snn), and second by transformation of the process itself by an adaptive low-dimensional subspace in which the update step on the residual can be performed in a space commensurate with the available information content of the process and measured response.

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Zhang, Yi, Xiaojing Shen, Zhiguo Wang, and Yunmin Zhu. "Random MHT data association algorithm based on random coefficient Kalman filter." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009766.

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Sungra, Anshul, and Brian Fabien. "Evaluation of Control Algorithms on Mobile Robots for Collision Avoidance." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23500.

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Abstract This paper describes the implementation of various algorithms to control the distance between a lead vehicle and a following (ego) vehicle. The ego robot equipped with a monocular camera and a rotating laser sensor (LDS). The monocular camera used for object detection using the Aggregate Channel Features (ACF) detection algorithm. The width of the bounding box generated by the detection algorithm had used to determine the distance between the lead and the following vehicles. Since this research focused on longitudinal autonomy, the data from the rotating laser sensor downsampled from 360 points to 30 points. These sampled points covered the front view of the vehicle. All data points transformed into a planar world coordinate (two-dimensional plane). The outputs of the camera and laser sensor (LDS) were fused to obtain accurate distance measurements for the lead vehicle. Sensor calibration had achieved by comparing sensor data with the ground truth values. Kalman Filter was used to implementing sensor fusion by combining perception data from the monocular camera and LDS for accurate position and velocity estimation. This calibration provided information about the sensor noise and deviation of sensor data from its ground truth values. These values helped to determine the error covariance matrixes of the Kalman filter. For implementation, the Robot Operating System (ROS)-MATLAB platform used to communicate between robot and host Personal Computer (PC). The experiments evaluated the performance of Proportional Control (P), Proportional-Integral Control (PI), and Model Predictive Control (MPC) in maintaining a minimum distance between the vehicles. For the MPC implementation in MATLAB, Model Predictive Control Quadratic Programming (MPCQP) solver was used to get the optimal solution for control output. The results show that the MPC yields faster response times when compared to P control and PI control. These algorithms evaluated during constant velocity and constant acceleration of the lead vehicle. The steady-state errors of P and PI controllers were around 0.1 meters (m) in both scenarios and 0 to 0.2m for constant velocity and 0 to 0.15m for ramp velocity, respectively. And for MPC, steady-state error varied from −0.05m to 0.05m in both the scenarios. This range in steady-state error was due to varying speed of the ego vehicle with time to maintain the minimum relative distance between the robots, and there was a communication delay in the system that also affected the behavior of the controllers. The MPC was more sensitive to communication delays. However, the effect of this communication delay was negligible to P and PI controllers. This sensitivity resulted in different velocity profiles for the ego vehicle in MPC and P or PI controllers.

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Zhang, Chaokun, and Huiying Wang. "Decentralized Multi-sensor Data Fusion Algorithm Using Information Filter." In 2010 International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2010). IEEE, 2010. http://dx.doi.org/10.1109/icmtma.2010.506.

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Cherchar, Ammar, Messaoud Thameri, and Adel Belouchrani. "A new multi-sensor fusion algorithm based on the Information Filter framework." In 2017 Seminar on Detection Systems Architectures and Technologies (DAT). IEEE, 2017. http://dx.doi.org/10.1109/dat.2017.7889154.

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Clark, J. M. C. "Projection filters and matched moment filters in tracking." In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications. IEE, 2008. http://dx.doi.org/10.1049/ic:20080065.

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Atherton, D. P. "Data fusion for several Kalman filters tracking a single target." In Target Tracking 2004: Algorithms and Applications. IEE, 2004. http://dx.doi.org/10.1049/ic:20040053.

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Gong, Ting. "Expression Recognition Method of Fusion Gabor Filter and 2DPCA Algorithm." In 2020 International Conference on Computer Information and Big Data Applications (CIBDA). IEEE, 2020. http://dx.doi.org/10.1109/cibda50819.2020.00121.

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Easthope, P. F. "Tracking Simulated UAV Swarms Using Particle Filters." In IET Conference on Data Fusion & Target Tracking 2014: Algorithms and Applications. Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.0524.

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