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The Impact Of The Quality Assessment Of Optimal Assignment For Data Association In A Multitarget Tracking Context
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Book Synopsis The Impact of the Quality Assessment of Optimal Assignment for Data Association in a Multitarget Tracking Context by : J. Dezert
Download or read book The Impact of the Quality Assessment of Optimal Assignment for Data Association in a Multitarget Tracking Context written by J. Dezert and published by Infinite Study. This book was released on with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main purpose of this paper is to apply and to test the performance of a new method, based on belief functions, proposed by Dezert et al. in order to evaluate the quality of the individual association pairings provided in the optimal data association solution for improving the performances of multisensormultitarget tracking systems.
Book Synopsis Multitarget Tracking Performance based on the Quality Assessment of Data Association by : Jean Dezert
Download or read book Multitarget Tracking Performance based on the Quality Assessment of Data Association written by Jean Dezert and published by Infinite Study. This book was released on with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main objective of this paper is to present, to apply, and to test the effectiveness of the new method, based on belief functions, proposed by Dezert et al. in order to evaluate the quality of the individual association pairings provided in the classical optimal data association solution for improving the performances of multitarget tracking systems in clutter, when some of the association decisions given in the optimal assignment solution are unreliable and doubtful and lead to potentially critical mistake. This evaluation is based on a Monte Carlo simulation for particular difficult maneuvering and nonmaneuvering MTT problems in clutter. A comparison with the results obtained on the base of Kinematic only Data Association and Generalized Data Association is made.
Book Synopsis Advances and Applications of DSmT for Information Fusion (Collected Works. Volume 5) by : Florentin Smarandache
Download or read book Advances and Applications of DSmT for Information Fusion (Collected Works. Volume 5) written by Florentin Smarandache and published by Infinite Study. This book was released on 2023-12-27 with total page 932 pages. Available in PDF, EPUB and Kindle. Book excerpt: This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 (available at fs.unm.edu/DSmT-book4.pdf or www.onera.fr/sites/default/files/297/2015-DSmT-Book4.pdf) in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well. We want to thank all the contributors of this fifth volume for their research works and their interests in the development of DSmT, and the belief functions. We are grateful as well to other colleagues for encouraging us to edit this fifth volume, and for sharing with us several ideas and for their questions and comments on DSmT through the years. We thank the International Society of Information Fusion (www.isif.org) for diffusing main research works related to information fusion (including DSmT) in the international fusion conferences series over the years. Florentin Smarandache is grateful to The University of New Mexico, U.S.A., that many times partially sponsored him to attend international conferences, workshops and seminars on Information Fusion. Jean Dezert is grateful to the Department of Information Processing and Systems (DTIS) of the French Aerospace Lab (Office National d’E´tudes et de Recherches Ae´rospatiales), Palaiseau, France, for encouraging him to carry on this research and for its financial support. Albena Tchamova is first of all grateful to Dr. Jean Dezert for the opportunity to be involved during more than 20 years to follow and share his smart and beautiful visions and ideas in the development of the powerful Dezert-Smarandache Theory for data fusion. She is also grateful to the Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, for sponsoring her to attend international conferences on Information Fusion.
Book Synopsis A Comparative Analysis of QADA-KF with JPDAF for Multitarget Tracking in Clutter by : Jean Dezert
Download or read book A Comparative Analysis of QADA-KF with JPDAF for Multitarget Tracking in Clutter written by Jean Dezert and published by Infinite Study. This book was released on with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents a comparative analysis of performances of two types of multi-target tracking algorithms: 1) the Joint Probabilistic Data Association Filter (JPDAF), and 2) classical Kalman Filter based algorithms for multi-target tracking improved with Quality Assessment of Data Association (QADA) method using optimal data association. The evaluation is based on Monte Carlo simulations for difficult maneuvering multiple-target tracking (MTT) problems in clutter.
Book Synopsis Generalized Data Association for Multitarget Tracking in Clutter by : A. Tchamova
Download or read book Generalized Data Association for Multitarget Tracking in Clutter written by A. Tchamova and published by Infinite Study. This book was released on with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this chapter is to present an approach for target track ing in cluttered environment, which incorporates the advanced concept of generalized data (kinematics and attribute) association (GDA) to improve track maintenance performance in complicated situations (closely spaced and/or crossing targets), when kinematics data are insufficient for correct decision making.
Book Synopsis Multitarget Tracking in Clutter based on Generalized Data Association: Performance Evaluation of Fusion Rules by : J. Dezert
Download or read book Multitarget Tracking in Clutter based on Generalized Data Association: Performance Evaluation of Fusion Rules written by J. Dezert and published by Infinite Study. This book was released on with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this chapter is to present and compare different fusion rules which can be used for Generalized Data Association (GDA) for multitarget tracking (MTT) in clutter.
Book Synopsis Performance Evaluation of Multitarget Tracking Algorithms by : Huimin Chen
Download or read book Performance Evaluation of Multitarget Tracking Algorithms written by Huimin Chen and published by . This book was released on 2002 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On Computationally Efficient Frameworks for Data Association in Multi-target Tracking by : Sriram Krishnaswamy (Ph. D. in mechanical engineering)
Download or read book On Computationally Efficient Frameworks for Data Association in Multi-target Tracking written by Sriram Krishnaswamy (Ph. D. in mechanical engineering) and published by . This book was released on 2019 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this dissertation is to examine ways of improving the computational efficiency of data association algorithms in tracking and to do so with better methods to handle data. Data association algorithms are employed in tracking problems in conjunction with an estimation algorithm to determine the optimal state estimate of multiple objects of interest given a set of measurements. This work primarily deals with Bayesian or pseudo-Bayesian paradigms for data association and reduces the computational cost by reducing the exponential growth or the so-called "curse of dimensionality'' in these problems. This increase in the number of hypotheses is exacerbated in dense environments with low signal-to-noise ratio (SNR). This research employs tensor decomposition to reduce the number of incoming measurements into a core tensor or a low-dimensional summary and use it as a substitute for the complete set of measurements.
Book Synopsis Performance evaluation of a tracking algorithm including attribute data by : Jean Dezert
Download or read book Performance evaluation of a tracking algorithm including attribute data written by Jean Dezert and published by Infinite Study. This book was released on with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main objective of this work is to investigate the impact of the quality of attribute data source on the performance of a target tracking algorithm. An array of dense scenarios arranged according to the distance between closely spaced targets is studied by different confusion matrices.
Book Synopsis Group-target Tracking by : Wen-dong Geng
Download or read book Group-target Tracking written by Wen-dong Geng and published by Springer. This book was released on 2016-10-01 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes grouping detection and initiation; group initiation algorithm based on geometry center; data association and track continuity; as well as separate-detection and situation cognition for group-target. It specifies the tracking of the target in different quantities and densities. At the same time, it integrates cognition into the application. Group-target Tracking is designed as a book for advanced-level students and researchers in the area of radar systems, information fusion of multi-sensors and electronic countermeasures. It is also a valuable reference resource for professionals working in this field.
Book Synopsis A Score for Correct Data Association in Multi-Target Tracking by : D. L. Alspach
Download or read book A Score for Correct Data Association in Multi-Target Tracking written by D. L. Alspach and published by . This book was released on 1979 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the real-world multi-target tracking problem, there exists the possibility for many things to go wrong. Typical problems which arise include: too few tracks are formed; too many tracks are formed (false tracks); and inaccurate position, course, and speed estimates are reported. The above difficulties are often the result of incorrect allocation of data to individual tracks. Algorithms, while estimating the motion of a given target, inadvertently mix in clutter and/or measurements from another target. In order for correct allocation of data to a given track to be made, one must have an effective scoring formula; that is, some means of determining how likely a given assignment of data is. To be effective, a scoring formula must produce (on the average) a better score for correct assignments than for incorrect assignments. Information useful in the scoring process includes a priori intelligence data (such as initial target locations), models of target motion, models of the transmission channel, and expected moments of clutter for the sensor gain setting being used. Basically, the score is derived from the residuals which come out of the processing of a batch of data with extended Kalman filter. This is used to evaluate the likelihood of potential tracks. Although the 'likelihood' has an intuitive meaning, the term is used here to mean the probability density function p(lambda) of the track lambda. The expected cost of a given assignment is derived with the theory of externals being used to obtain the expected cost of adding a clutter point in a track. The resulting expected cost is then shown to behave in a quantitative fashion and this can be visualized from a geometric viewpoint. (Author).
Book Synopsis Optimization Problems in Multitarget/Multisensor Tracking by :
Download or read book Optimization Problems in Multitarget/Multisensor Tracking written by and published by . This book was released on 1997 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ever-increasing demand in surveillance is to produce highly accurate target and track identification and estimation in real-time, even for dense target scenarios and in regions of high track contention. The use of multiple sensors, through more varied information, has the potential to greatly enhance target identification and state estimation. For multitarget tracking, the processing of multiple scans all at once yields the desired track identification and accurate state estimation; however, one must solve an NP-hard data association problem of partitioning observations into tracks and false alarms in real-time. This report summarizes the development of a multisensor-multitarget tracker based on the use of near-optimal and real-time algorithms for the data association problem and is divided into several parts. The first part addresses the formulation of multisensor and multiscan processing of the data association problem as a combinatorial optimization problem. The new algorithms under development for this NP-hard problem are based on a recursive Lagrangian relaxation scheme, construct near-optimal solutions in real-time, and use a variety of techniques such as two-dimensional assignment algorithms, a bundle trust region method for the nonsmooth optimization, and graph theoretic algorithms for problem decomposition. A brief computational complexity analysis as well as a comparison with some additional heuristic and optimal algorithms is included to demonstrate the efficiency of the algorithms. New results on numerical efficiency and increased robustness for track maintenance are also discussed. This program has produced two U.S. patents with a third pending and has developed the basis for the IBest of Breed Tracker Contest winner at Hanscom AFB in 1996.
Book Synopsis Nonlinear Assignment Problems by : Panos M. Pardalos
Download or read book Nonlinear Assignment Problems written by Panos M. Pardalos and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear Assignment Problems (NAPs) are natural extensions of the classic Linear Assignment Problem, and despite the efforts of many researchers over the past three decades, they still remain some of the hardest combinatorial optimization problems to solve exactly. The purpose of this book is to provide in a single volume, major algorithmic aspects and applications of NAPs as contributed by leading international experts. The chapters included in this book are concerned with major applications and the latest algorithmic solution approaches for NAPs. Approximation algorithms, polyhedral methods, semidefinite programming approaches and heuristic procedures for NAPs are included, while applications of this problem class in the areas of multiple-target tracking in the context of military surveillance systems, of experimental high energy physics, and of parallel processing are presented. Audience: Researchers and graduate students in the areas of combinatorial optimization, mathematical programming, operations research, physics, and computer science.
Book Synopsis Data Association Algorithms for Multiple Target Tracking by : J. C. McMillan
Download or read book Data Association Algorithms for Multiple Target Tracking written by J. C. McMillan and published by . This book was released on 1990 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Optimization Problems in Multisensor and Multitarget Tracking by :
Download or read book Optimization Problems in Multisensor and Multitarget Tracking written by and published by . This book was released on 2008 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this research program is to develop optimization algorithms that solve key problems in multiple target tracking and sensor data fusion. The central problem in multiple target tracking is the data association problem of partitioning sensor reports into tracks and false alarms. New classes of data association problems have been formulated and initial algorithms developed to address cluster tracking, merged measurements, and even sensor resource management in the form of "group-assignments." In a different direction, an efficient k-best algorithm has been developed to approximate the uncertainty in data association, which is ontical for discrimination or combat identification. Statistical Monte Carlo methods are also applicable and are still under investigation. Bias estimation algorithms using known data association such as truth objects and targets of opportunity have been developed. Bias estimation in which data association is unknown is difficult due to the nonconvex and mixed integer nature of the mathematical formulation. Exact and approximate algorithms have been developed and successfully applied to system tracking. As a prerequisite to the development of multiple target tracking approaches to space surveillance, consistent measures of uncertainty for initial orbit determination and the propagation of the uncertainty over time have been developed.
Download or read book Science Abstracts written by and published by . This book was released on 1993 with total page 980 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Data Association Algorithms for Early Detection and Efficient Assignment in Target Tracking by : Muhammad Riad Chummun
Download or read book Data Association Algorithms for Early Detection and Efficient Assignment in Target Tracking written by Muhammad Riad Chummun and published by . This book was released on 1999 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:
