Download or read book Tracking of Multiple Maneuvering Targets in Clutter Using Multiple Sensors IMM and JPDA Coupled Filtering written by and published by . This book was released on 1998 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider the problem of tracking multiple maneuvering targets in clutter using switching multiple target motion models. A novel suboptimal filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach, the joint probabilistic data association (JPDA) technique and coupled target state estimation to a Markovian switching system. In past such an approach has been considered using uncoupled target state estimation. The algorithm is illustrated via a simulation example involving tracking of two highly maneuvering, at times closely spaced, targets. In the presented example, the proposed IMM/JPDA coupled filter outperforms an existing IMM/JPDA uncoupled filter.
Download or read book Tracking of Multiple Maneuvering Targets Using Multiscan JPDA and IMM Filtering written by and published by . This book was released on 2003 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider the problem of tracking multiple maneuvering targets in the presence of clutter using switching multiple target motion models. A novel suboptimal filtering algorithm is developed by applying the basic interacting multiple model (IMM) approach and joint probability data association technique. But unlike the standard single scan joint probabilistic data association (JPDA) approach, we exploit a multiscan joint probabilistic data association (Mscan-JPDA) approach to solve the data association problem. The algorithm is illustrated via a simulation example involving training of three maneuvering targets and a multiscan data window of length two.
Download or read book Parallel Detection Fusion for Multisensor Tracking of a Maneuvering Target in Clutter Using IMMPDA Filtering written by and published by . This book was released on 2003 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a (suboptimal) filtering algorithm for tracking a highly maneuvering target in a cluttered environment using multiple sensors. The filtering algorithm is developed by applying the basic Interacting Multiple Model (IMM) approach and the Probabilistic Data Association (PDA) technique to a two sensor (radar and infrared, for instance) problem for state estimation for the target. A detection fusion approach is followed where the raw sensor measurements are passed to a fusion node and fed directly to the target tracker. A multisensor probabilistic data association filter is developed for parallel sensor processing for target tracking under clutter. A past approach using parallel sensor processing has ignored certain data association probabilities leading to an erroneous derivation. Another existing approach applies only to non-maneuvering targets. The algorithm is illustrated via a highly maneuvering target tracking simulation example where two sensors, a radar and an infrared sensor, are used. Compared with an existing IMMPDA filtering algorithm with sequential sensor processing, the proposed algorithm achieves significant improvement in the accuracy of track estimation.
Download or read book Multisensor Tracking of a Maneuvering Target in Clutter with Asychronous Measurements Using IMMPDA Filtering and Parallel Detection Fusion written by and published by . This book was released on 2003 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a (suboptimal) filtering algorithm for tracking a highly maneuvering target in a cluttered environment using multiple sensors dealing with possibly asynchronous (time delayed) measurements. The filtering algorithm is developed by applying the basic Interacting Multiple Model (IMM) approach, the Probabilistic Data Association (PDA) technique, and asynchronous measurement updating for state-augmented system estimation for the target. A state augmented approach is developed to estimate the time delay between local and remote sensors. A multi- sensor probabilistic data association filter is developed for parallel sensor processing for target tracking under clutter. The algorithm is illustrated via a highly maneuvering target tracking simulation example where two sensors, a radar and an infrared sensor, are used. Compared with an existing IMMPDA filtering algorithm with the assumption of synchronous (no delay) measurements sensor processing, the proposed algorithm achieves considerable improvement (especially in the case of larger delays) in the accuracy of track estimation.
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.
Download or read book Algorithms for Tracking Single Maneuvering and Multiple Closely Spaced Targets written by Mohamed Nabil Abdelghaffar Eltoukhy and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Target tracking is crucial in monitoring and controlling air traffic in civilian and military applications. Target tracking is a process of estimating the current position and predict the future position of one or more targets using the measurements received by a radar system. One of the major challenges in tracking a single target is when it performs a maneuver and the angle of maneuver is not known. The interacting multiple model (IMM) algorithm is the most commonly-used algorithm for tracking a maneuvering target with an a priori knowledge of the target turn rate, since it provides a very good tracking performance with moderate complexity. However, the tracking performance of such an algorithm deteriorates or may even fail when the target performs a maneuver with a turn rate larger than that assumed in the design of the algorithm. A few methods have been reported to overcome this limitation of an assumed turn rate by actually estimating it adaptively. Two of such algorithms use nonlinear filters that leads to a large complexity, and one of them uses linear filters and models providing good tracking performance, but only for mild maneuvers. For tracking multiple targets, several algorithms have been proposed, among which the joint probability data association (JPDA) algorithm is considered to be the best algorithm, since it provides good tracking performance when the targets are widely spaced. However, the tracking performance of this algorithm deteriorates, and coalescence of the tracks may occur, when the targets are closely spaced. Some efforts have been made to overcome the problem of tracking closely spaced targets by ignoring the target identity, but at the expense of very large complexity. The work of this thesis is carried out in two parts. In the first part, two algorithms within the IMM framework are proposed to track a single maneuvering target, when the target turn rate is not known a priori. In both the algorithms, the turn rate is dynamically estimated using noisy measurements. In the first algorithm, the turn rate at each time instant k is estimated based on the target speed and the radius of the circle formed by the measurement at that instant and the two previous consecutive noisy measurements, (k-1) and (k-2). The segment of this circle covered by these three noisy measurements is used to model the true track of the target at the instant time k. In the second algorithm, the accuracy of the turn rate estimated in the first algorithm is improved using the information on the level of the measurement noise. In the second part of the thesis, a systematic study on the impact of the spacing between the targets as well as when the targets make abrupt turns with sharp angles on the tracking performance of the JPDA algorithm is conducted. Then, a new algorithm for tracking multiple targets based on the spatial distribution of the measurements for determining the weights for measurement-target association is proposed within the JPDA framework. The proposed algorithm for multiple target tracking is designed to deal with the problems of closely spaced targets and their abrupt sharp turns more effectively. Effectiveness and superiority of the algorithms proposed for tracking single and multiple targets are demonstrated through extensive experiments with a wide variety of different scenarios for target motions.
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.
Download or read book Manufacturing Science and Technology ICMST2011 written by Wu Fan and published by Trans Tech Publications Ltd. This book was released on 2011-11-22 with total page 8340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume is indexed by Thomson Reuters CPCI-S (WoS). The objective of ICMST 2011 was to provide a platform where researchers, engineers, academics and industrial professionals from all over the world could present their research results and discuss developments in Manufacturing Science and Technology. This conference provided opportunities for delegates to exchange new ideas and applications face-to-face, to establish business or research contacts and to find global partners for future collaboration.
Download or read book EVALUATION OF MULTI TARGET TRACKING ALGORITHMS IN THE PRESENCE OF CLUTTER written by and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT EVALUATION OF MULTI TARGET TRACKING ALGORITHMS IN THE PRESENCE OF CLUTTER Güner, Onur M.S., Department of Electrical and Electronics Engineering Supervisor: Prof. Dr. Mustafa Kuzuoðlu August 2005, 88 Pages This thesis describes the theoretical bases, implementation and testing of a multi target tracking approach in radar applications. The main concern in this thesis is the evaluation of the performance of tracking algorithms in the presence of false alarms due to clutter. Multi target tracking algorithms are composed of three main parts: track initiation, data association and estimation. Two methods are proposed for track initiation in this work. First one is the track score function followed by a threshold comparison and the second one is the 2/2 & M/N method which is based on the number of detections. For data association problem, several algorithms are developed according to the environment and number of tracks that are of interest. The simplest method for data association is the nearest-neighbor data association technique. In addition, the methods that use multiple hypotheses like probabilistic data association and joint probabilistic data association are introduced and investigated. Moreover, in the observation to track assignment, gating is an important issue since it reduces the complexity of the computations. Generally, ellipsoidal gates are used for this purpose. For estimation, Kalman filters are used for state prediction and measurement update. In filtering, target kinematics is an important point for the modeling. Therefore, Kalman filters based on different target kinematic models are run in parallel and the outputs of filters are combined to yield a single solution. This method is developed for maneuvering targets and is called interactive multiple modeling (IMM). All these algorithms are integrated to form a multi target tracker that works in the presence (or absence) of clutter. Track score function, joint probabilistic data association (JPD.
Download or read book Multiple Model Adaptive Estimator Target Tracker for Maneuvering Targets in Clutter written by Brian D. Smith and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Target Tracking with Random Finite Sets written by Weihua Wu and published by Springer Nature. This book was released on 2023-08-02 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on target tracking and information fusion with random finite sets. Both principles and implementations have been addressed, with more weight placed on engineering implementations. This is achieved by providing in-depth study on a number of major topics such as the probability hypothesis density (PHD), cardinalized PHD, multi-Bernoulli (MB), labeled MB (LMB), d-generalized LMB (d-GLMB), marginalized d-GLMB, together with their Gaussian mixture and sequential Monte Carlo implementations. Five extended applications are covered, which are maneuvering target tracking, target tracking for Doppler radars, track-before-detect for dim targets, target tracking with non-standard measurements, and target tracking with multiple distributed sensors. The comprehensive and systematic summarization in target tracking with RFSs is one of the major features of the book, which is particularly suited for readers who are interested to learn solutions in target tracking with RFSs. The book benefits researchers, engineers, and graduate students in the fields of random finite sets, target tracking, sensor fusion/data fusion/information fusion, etc.
Download or read book Intelligent Information Processing III written by K. Shimohara and published by Springer Science & Business Media. This book was released on 2007-11-14 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Information Processing supports the most advanced productive tools that are said to be able to change human life and the world itself. This book presents the proceedings of the 4th IFIP International Conference on Intelligent Information Processing. This conference provides a forum for engineers and scientists in academia, university and industry to present their latest research findings in all aspects of Intelligent Information Processing.
Download or read book Multi Camera Networks written by Hamid Aghajan and published by Academic Press. This book was released on 2009-04-25 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book, by the leading experts, on this rapidly developing field with applications to security, smart homes, multimedia, and environmental monitoring Comprehensive coverage of fundamentals, algorithms, design methodologies, system implementation issues, architectures, and applications Presents in detail the latest developments in multi-camera calibration, active and heterogeneous camera networks, multi-camera object and event detection, tracking, coding, smart camera architecture and middleware This book is the definitive reference in multi-camera networks. It gives clear guidance on the conceptual and implementation issues involved in the design and operation of multi-camera networks, as well as presenting the state-of-the-art in hardware, algorithms and system development. The book is broad in scope, covering smart camera architectures, embedded processing, sensor fusion and middleware, calibration and topology, network-based detection and tracking, and applications in distributed and collaborative methods in camera networks. This book will be an ideal reference for university researchers, R&D engineers, computer engineers, and graduate students working in signal and video processing, computer vision, and sensor networks. Hamid Aghajan is a Professor of Electrical Engineering (consulting) at Stanford University. His research is on multi-camera networks for smart environments with application to smart homes, assisted living and well being, meeting rooms, and avatar-based communication and social interactions. He is Editor-in-Chief of Journal of Ambient Intelligence and Smart Environments, and was general chair of ACM/IEEE ICDSC 2008. Andrea Cavallaro is Reader (Associate Professor) at Queen Mary, University of London (QMUL). His research is on target tracking and audiovisual content analysis for advanced surveillance and multi-sensor systems. He serves as Associate Editor of the IEEE Signal Processing Magazine and the IEEE Trans. on Multimedia, and has been general chair of IEEE AVSS 2007, ACM/IEEE ICDSC 2009 and BMVC 2009. The first book, by the leading experts, on this rapidly developing field with applications to security, smart homes, multimedia, and environmental monitoring Comprehensive coverage of fundamentals, algorithms, design methodologies, system implementation issues, architectures, and applications Presents in detail the latest developments in multi-camera calibration, active and heterogeneous camera networks, multi-camera object and event detection, tracking, coding, smart camera architecture and middleware
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.
Download or read book Multitarget multisensor Tracking Applications and advances written by Yaakov Bar-Shalom and published by . This book was released on 1990 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Target Tracking in Environments of Rapidly Changing Clutter written by Karl Dutson and published by . This book was released on 2015 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tracking targets in the presence of clutter is inevitable, and presents many challenges. Additionally, rapid, drastic changes in clutter density between different environments or scenarios can make it even more difficult for tracking algorithms to adapt. A novel approach to target tracking in such dynamic clutter environments is proposed using a particle filter (PF) integrated with Interacting Multiple Models (IMMs) to compensate and adapt to the transition between different clutter densities. This model was implemented for the case of a monostatic sensor tracking a single target moving with constant velocity along a two-dimensional trajectory, which crossed between regions of drastically different clutter densities. Multiple combinations of clutter density transitions were considered, using up to three different clutter densities. It was shown that the integrated IMM PF algorithm outperforms traditional approaches such as the PF in terms of tracking results and performance. The minimal additional computational expense of including the IMM more than warrants the benefits of having it supplement and amplify the advantages of the PF.
Download or read book Multiple Scan Joint Probabilistic Data and Maneuver Association written by Brian Viet Lieu and published by . This book was released on 1993 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt:
