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Languages : en
Pages : 0
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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.
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Languages : en
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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.
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Languages : en
Pages : 31
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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.
Author: Yaakov Bar-Shalom
Publisher:
ISBN: 9780964831209
Category : Radar
Languages : en
Pages : 615
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Category :
Languages : en
Pages : 12
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The central problem in any surveillance system is the data association problem of partitioning observations into tracks and false alarms. Over the last fifteen years and with support from AFOSR, a new approach has been developed based on the use of multi-dimensional assignment problem formulation and Lagrangian relaxation algorithms. (This approach is often called multiple frame assignments or MFA for short.) Four U.S. patents have now been issued for this work. What is more, based on this new technology, Lockheed Martin of Oswego, NY won the best of Breed Tracking Contest for the next upgrade to AWACS held at Hanscom AFB in Boston in 1996, and it has been chosen as the tracking system for the Navy's new multipurpose helicopter under the LAMPS program. Currently, it is a contender for national and ballistic missile defense in the Hercules Program funded by MD Advanced Systems, for STSS Program as funded by the Department of the Air Force (in 2001 and 2002) and MDA in 2003.
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Languages : en
Pages : 0
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This report results from a contract tasking Technical University of Crete as follows: I. Construction of a set of problem instances of multidimensional assignment problems in the context of target tracking. These will be used as benchmark problems. They will be constructed so that their optimal solution will be known, and they will vary in size and dimension. Furthermore they will be nontrivial to solve, since they will be used for evaluation of the proposed algorithms in the experimental runs. 2. Design and implementation of data structures to represent the massive sparse data sets associated with each instance of the problem. These data structures will be general enough to handle variable dimension and degrees of sparsity. Specific tasks to be performed by the algorithms, such as function evaluation and construction of feasible and partial solutions, should require minimum computational effort and memory. 3. Design and implementation of heuristic and exact algorithms for solving the multidimensional assignment problem. The heuristic algorithm will receive the dimension of the instance and the sparse multidimensional array as inputs, and it will provide the partitions that represent the targets. The exact algorithm will use a branch-and-bound scheme to provide exact solutions to the problem. All the codes will be written using the C programming language.
Author: Yaakov Bar-Shalom
Publisher:
ISBN:
Category : Radar
Languages : en
Pages : 474
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Author: Shozo Mori
Publisher:
ISBN:
Category :
Languages : en
Pages : 69
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Based upon a general target sensor model which allows dependence among targets and state-dependent target detection, a Bayesian solution to the multitarget tracking problem is derived. When this solution is applied to a special class of models, a less general but more implementationally feasible class of algorithms is obtained. Representative existing algorithms are then compared with our results. Doing so provides a unified view on Bayesian approaches to the multitarget tracking problem. Part I covers most of the analytical results, while in Part II, hypothesis management and other issues pertaining to implementation of multitarget algorithms are discussed with several examples. (jd/rh).
Author: Yunmin Zhu
Publisher: Springer Science & Business Media
ISBN: 1461510457
Category : Technology & Engineering
Languages : en
Pages : 248
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YUNMIN ZHU In the past two decades, multi sensor or multi-source information fusion tech niques have attracted more and more attention in practice, where observations are processed in a distributed manner and decisions or estimates are made at the individual processors, and processed data (or compressed observations) are then transmitted to a fusion center where the final global decision or estimate is made. A system with multiple distributed sensors has many advantages over one with a single sensor. These include an increase in the capability, reliability, robustness and survivability of the system. Distributed decision or estimation fusion prob lems for cases with statistically independent observations or observation noises have received significant attention (see Varshney's book Distributed Detec tion and Data Fusion, New York: Springer-Verlag, 1997, Bar-Shalom's book Multitarget-Multisensor Tracking: Advanced Applications, vol. 1-3, Artech House, 1990, 1992,2000). Problems with statistically dependent observations or observation noises are more difficult and have received much less study. In practice, however, one often sees decision or estimation fusion problems with statistically dependent observations or observation noises. For instance, when several sensors are used to detect a random signal in the presence of observation noise, the sensor observations could not be statistically independent when the signal is present. This book provides a more complete treatment of the fundamentals of multi sensor decision and estimation fusion in order to deal with general random ob servations or observation noises that are correlated across the sensors.
Author: William W. Hager
Publisher: Springer Science & Business Media
ISBN: 1461336325
Category : Mathematics
Languages : en
Pages : 470
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On February 15-17, 1993, a conference on Large Scale Optimization, hosted by the Center for Applied Optimization, was held at the University of Florida. The con ference was supported by the National Science Foundation, the U. S. Army Research Office, and the University of Florida, with endorsements from SIAM, MPS, ORSA and IMACS. Forty one invited speakers presented papers on mathematical program ming and optimal control topics with an emphasis on algorithm development, real world applications and numerical results. Participants from Canada, Japan, Sweden, The Netherlands, Germany, Belgium, Greece, and Denmark gave the meeting an important international component. At tendees also included representatives from IBM, American Airlines, US Air, United Parcel Serice, AT & T Bell Labs, Thinking Machines, Army High Performance Com puting Research Center, and Argonne National Laboratory. In addition, the NSF sponsored attendance of thirteen graduate students from universities in the United States and abroad. Accurate modeling of scientific problems often leads to the formulation of large scale optimization problems involving thousands of continuous and/or discrete vari ables. Large scale optimization has seen a dramatic increase in activities in the past decade. This has been a natural consequence of new algorithmic developments and of the increased power of computers. For example, decomposition ideas proposed by G. Dantzig and P. Wolfe in the 1960's, are now implement able in distributed process ing systems, and today many optimization codes have been implemented on parallel machines.
Author: Martin Liggins II
Publisher: CRC Press
ISBN: 1420053094
Category : Technology & Engineering
Languages : en
Pages : 872
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In the years since the bestselling first edition, fusion research and applications have adapted to service-oriented architectures and pushed the boundaries of situational modeling in human behavior, expanding into fields such as chemical and biological sensing, crisis management, and intelligent buildings. Handbook of Multisensor Data Fusion: Theory and Practice, Second Edition represents the most current concepts and theory as information fusion expands into the realm of network-centric architectures. It reflects new developments in distributed and detection fusion, situation and impact awareness in complex applications, and human cognitive concepts. With contributions from the world’s leading fusion experts, this second edition expands to 31 chapters covering the fundamental theory and cutting-edge developments that are driving this field. New to the Second Edition— · Applications in electromagnetic systems and chemical and biological sensors · Army command and combat identification techniques · Techniques for automated reasoning · Advances in Kalman filtering · Fusion in a network centric environment · Service-oriented architecture concepts · Intelligent agents for improved decision making · Commercial off-the-shelf (COTS) software tools From basic information to state-of-the-art theories, this second edition continues to be a unique, comprehensive, and up-to-date resource for data fusion systems designers.
