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Optimization Problems In Multisensor And Multitarget Target Tracking
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Book Synopsis Optimization Problems in Multisensor and Multitarget Target Tracking by :
Download or read book Optimization Problems in Multisensor and Multitarget Target Tracking written by and published by . This book was released on 2004 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
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.
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 1995 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: A multi-sensor multi-target tracker based on the use of near optimal and real-time algorithms for data association has been developed.
Book Synopsis Multi-target Tracking Via Mixed Integer Optimization by : Zachary Clayton Saunders
Download or read book Multi-target Tracking Via Mixed Integer Optimization written by Zachary Clayton Saunders and published by . This book was released on 2016 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given a set of target detections over several time periods, this paper addresses the multi-target tracking problem (MTT) of optimally assigning detections to targets and estimating the trajectory of the targets over time. MTT has been studied in the literature via predominantly probabilistic methods. In contrast to these approaches, we propose the use of mixed integer optimization (MIO) models and local search algorithms that are (a) scalable, as they provide near optimal solutions for six targets and ten time periods in milliseconds to seconds, (b) general, as they make no assumptions on the data, (c) robust, as they can accommodate missed and false detections of the targets, and (d) easily implementable, as they use at most two tuning parameters. We evaluate the performance of the new methods using a novel metric for complexity of an instance and find that they provide high quality solutions both reliably and quickly for a large range of scenarios, resulting in a promising approach to the area of MTT.
Book Synopsis Advanced Algorithms for Multi-Sensor Multi-Target Tracking by : Sumedh Puranik
Download or read book Advanced Algorithms for Multi-Sensor Multi-Target Tracking written by Sumedh Puranik and published by LAP Lambert Academic Publishing. This book was released on 2010 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Target tracking has tremendous applications in both military and civilian surveillance systems. Typical applications are satellite surveillance systems, air-traffic control, undersea surveillance, sophisticated weapon delivery systems, global positioning systems, etc. The rapid developments in hardware and software technology have increased the signal processing capabilities of these surveillance systems. Advances in sensing resources have made possible to collect the enormous and complex amount of observation data from the targets. This has generated a continuing need for further development in information processing capabilities of these systems. Besides that, target tracking is as such a very complex problem. Complexity of the overall tracking problem increases substantially with the presence of maneuvering target, multiple targets, multiple distributed sensors, and background noise or clutter. In this book we develop a set of new suboptimal filtering and smoothing algorithms for maneuvering target tracking application. The proposed algorithms provide better performance in terms of estimation accuracy over the existing algorithms.
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 Multisensor Data Association and Resource Management for Target Tracking by : Thiagalingam Kirubarajan
Download or read book Multisensor Data Association and Resource Management for Target Tracking written by Thiagalingam Kirubarajan and published by . This book was released on 1998 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multitarget-multisensor Tracking by : Yaakov Bar-Shalom
Download or read book Multitarget-multisensor Tracking written by Yaakov Bar-Shalom and published by . This book was released on 1995 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Stochastic Optimization Framework for Stable Multi-target Tracking by : Ting Yueh Jeng
Download or read book A Stochastic Optimization Framework for Stable Multi-target Tracking written by Ting Yueh Jeng and published by . This book was released on 2009 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Optimum Techniques Multi-sensor Multi-target Tracking and Track Association by : Evangelos H. Giannopoulos
Download or read book Optimum Techniques Multi-sensor Multi-target Tracking and Track Association written by Evangelos H. Giannopoulos and published by . This book was released on 1999 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Algorithms for Multitarget Multisensor Tracking by :
Download or read book Algorithms for Multitarget Multisensor Tracking written by and published by . This book was released on 2003 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Multi-sensor Multi-target Data Fusion, Tracking and Identification Techniques for Guidance and Control Applications by :
Download or read book Multi-sensor Multi-target Data Fusion, Tracking and Identification Techniques for Guidance and Control Applications written by and published by . This book was released on 1996 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Resumé på fransk.
Book Synopsis Multi-sensor Target Tracking by : Jun Ye Yu
Download or read book Multi-sensor Target Tracking written by Jun Ye Yu and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Target tracking is a well-studied research topic with a vast array of applications. The basic idea is to track one or more targets of interest using data collected by one or more sensors. While a single sensor may provide enough data, it is more beneficial to establish a network of sensors that collaborate with each other. In this thesis, we study multi-sensor target tracking and present three manuscripts.In the first manuscript, we present a distributed bearings-only single-target particle filter. Unlike the existing literature, the proposed filter incorporates the Earth's curvature in the measurement model to provide more accurate bearing computation. Furthermore, we derive an approximate joint log-likelihood function to reduce the total communication overhead. In the second manuscript, we extend our work in the first manuscript and present two compression algorithms for distributed particle filters. The proposed algorithms construct a graph over the particles and exploit the resulting graph Laplacian matrix to encode the particle log-likelihoods. The proposed algorithms are not limited to any measurement models and can be incorporated in any generic particle filter. We also derive theoretical results showing that the proposed algorithms outperform existing methods at low communication overhead. In the third manuscript, we study data assignment in multi-target tracking. We propose two heuristic but computationally efficient algorithms for multi-sensor multi-target data assignment that can generate a number of likely target-measurement associations. We also implement these algorithms in a generalized labeled multi-Bernoulli filter to validate their performance." --
Book Synopsis Multitarget Multisensor Tracking Problems. Part 1. A General Solution and a Unified View on Bayesian Approaches by : Shozo Mori
Download or read book Multitarget Multisensor Tracking Problems. Part 1. A General Solution and a Unified View on Bayesian Approaches written by Shozo Mori and published by . This book was released on 1984 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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).
Book Synopsis Distributed Target Engagement in Large-scale Mobile Sensor Networks by : Samaneh Hosseini Semnani
Download or read book Distributed Target Engagement in Large-scale Mobile Sensor Networks written by Samaneh Hosseini Semnani and published by . This book was released on 2015 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensor networks comprise an emerging field of study that is expected to touch many aspects of our life. Research in this area was originally motivated by military applications. Afterward sensor networks have demonstrated tremendous promise in many other applications such as infrastructure security, environment and habitat monitoring, industrial sensing, traffic control, and surveillance applications. One key challenge in large-scale sensor networks is the efficient use of the network's resources to collect information about objects in a given Volume of Interest (VOI). Multi-sensor Multi-target tracking in surveillance applications is an example where the success of the network to track targets in a given volume of interest, efficiently and effectively, hinges significantly on the network's ability to allocate the right set of sensors to the right set of targets so as to achieve optimal performance. This task can be even more complicated if the surveillance application is such that the sensors and targets are expected to be mobile. To ensure timely tracking of targets in a given volume of interest, the surveillance sensor network needs to maintain engagement with all targets in this volume. Thus the network must be able to perform the following real-time tasks: 1) sensor-to-target allocation; 2) target tracking; 3) sensor mobility control and coordination. In this research I propose a combination of the Semi-Flocking algorithm, as a multi-target motion control and coordination approach, and a hierarchical Distributed Constraint Optimization Problem (DCOP) modelling algorithm, as an allocation approach, to tackle target engagement problem in large-scale mobile multi-target multi-sensor surveillance systems. Sensor-to-target allocation is an NP-hard problem. Thus, for sensor networks to succeed in such application, an efficient approach that can tackle this NP-hard problem in real-time is disparately needed. This research work proposes a novel approach to tackle this issue by modelling the problem as a Hierarchical DCOP. Although DCOPs has been proven to be both general and efficient they tend to be computationally expensive, and often intractable for large-scale problems. To address this challenge, this research proposes to divide the sensor-to-target allocation problem into smaller sub-DCOPs with shared constraints, eliminating significant computational and communication costs. Furthermore, a non-binary variable modelling is presented to reduce the number of inter-agent constraints. Target tracking and sensor mobility control and coordination are the other main challenges in these networks. Biologically inspired approaches have recently gained significant attention as a tool to address this issue. These approaches are exemplified by the two well-known algorithms, namely, the Flocking algorithm and the Anti-Flocking algorithm. Generally speaking, although these two biologically inspired algorithms have demonstrated promising performance, they expose deficiencies when it comes to their ability to maintain simultaneous reliable dynamic area coverage and target coverage. To address this challenge, Semi-Flocking, a biologically inspired algorithm that benefits from key characteristics of both the Flocking and Anti-Flocking algorithms, is proposed. The Semi-Flocking algorithm approaches the problem by assigning a small flock of sensors to each target, while at the same time leaving some sensors free to explore the environment. Also, this thesis presents an extension of the Semi-Flocking in which it is combined with a constrained clustering approach to provide better coverage over maneuverable targets. To have a reliable target tracking, another extension of Semi-Flocking algorithm is presented which is a coupled distributed estimation and motion control algorithm. In this extension the Semi-Flocking algorithm is employed for the purpose of a multi-target motion control, and Kalman-Consensus Filter (KCF) for the purpose of motion estimation. Finally, this research will show that the proposed Hierarchical DCOP algorithm can be elegantly combined with the Semi-Flocking algorithm and its extensions to create a coupled control and allocation approach. Several experimental analysis conducted in this research illustrate how the operation of the proposed algorithms outperforms other approaches in terms of incurred computational and communication costs, area coverage, target coverage for both linear and maneuverable targets, target detection time, number of undetected targets and target coverage in noise conditions sensor network. Also it is illustrated that this algorithmic combination can successfully engage multiple sensors to multiple mobile targets such that the number of uncovered targets is minimized and the sensors' mean utilization factor sensor surveillance systems.is maximized.
Book Synopsis On Informative Path Planning for Tracking and Surveillance by : Per Boström-Rost
Download or read book On Informative Path Planning for Tracking and Surveillance written by Per Boström-Rost and published by Linköping University Electronic Press. This book was released on 2019-05-23 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis studies a class of sensor management problems called informative path planning (IPP). Sensor management refers to the problem of optimizing control inputs for sensor systems in dynamic environments in order to achieve operational objectives. The problems are commonly formulated as stochastic optimal control problems, where to objective is to maximize the information gained from future measurements. In IPP, the control inputs affect the movement of the sensor platforms, and the goal is to compute trajectories from where the sensors can obtain measurements that maximize the estimation performance. The core challenge lies in making decisions based on the predicted utility of future measurements. In linear Gaussian settings, the estimation performance is independent of the actual measurements. This means that IPP becomes a deterministic optimal control problem, for which standard numerical optimization techniques can be applied. This is exploited in the first part of this thesis. A surveillance application is considered, where a mobile sensor is gathering information about features of interest while avoiding being tracked by an adversarial observer. The problem is formulated as an optimization problem that allows for a trade-off between informativeness and stealth. We formulate a theorem that makes it possible to reformulate a class of nonconvex optimization problems with matrix-valued variables as convex optimization problems. This theorem is then used to prove that the seemingly intractable IPP problem can be solved to global optimality using off-the-shelf optimization tools. The second part of this thesis considers tracking of a maneuvering target using a mobile sensor with limited field of view. The problem is formulated as an IPP problem, where the goal is to generate a sensor trajectory that maximizes the expected tracking performance, captured by a measure of the covariance matrix of the target state estimate. When the measurements are nonlinear functions of the target state, the tracking performance depends on the actual measurements, which depend on the target’s trajectory. Since these are unavailable in the planning stage, the problem becomes a stochastic optimal control problem. An approximation of the problem based on deterministic sampling of the distribution of the predicted target trajectory is proposed. It is demonstrated in a simulation study that the proposed method significantly increases the tracking performance compared to a conventional approach that neglects the uncertainty in the future target trajectory.
Book Synopsis Non-Cooperative Target Tracking, Fusion and Control by : Zhongliang Jing
Download or read book Non-Cooperative Target Tracking, Fusion and Control written by Zhongliang Jing and published by Springer. This book was released on 2018-06-25 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives a concise and comprehensive overview of non-cooperative target tracking, fusion and control. Focusing on algorithms rather than theories for non-cooperative targets including air and space-borne targets, this work explores a number of advanced techniques, including Gaussian mixture cardinalized probability hypothesis density (CPHD) filter, optimization on manifold, construction of filter banks and tight frames, structured sparse representation, and others. Containing a variety of illustrative and computational examples, Non-cooperative Target Tracking, Fusion and Control will be useful for students as well as engineers with an interest in information fusion, aerospace applications, radar data processing and remote sensing.
