Download or read book Multi target Tracking Algorithms for Cluttered Environments written by Marco Antonio Mayor and published by . This book was released on 1987 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

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 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:

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 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.

Download or read book Implementation of Target Tracking Algorithms for a Cluttered Environment Using Transputers written by Sohail Rabbani Khan and published by . This book was released on 1993 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Tracking Multiple Targets in Cluttered Environments with the Probabilistic Multi Hypothesis Tracking Filter written by Darin T. Dunham and published by . This book was released on 1997-03-01 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tracking multiple targets in a cluttered environment is extremely difficult. Traditional approaches generally use simple techniques that combine gating with some form of nearest neighbor association to reduce the effects of clutter. When clutter densities increase, these traditional algorithms fail to perform well. To counter this problem, the multi-hypothesis tracking (MHT) algorithm was developed. This approach enumerates almost every conceivable combination of measurements to determine the most likely tracks. This process quickly becomes very complex and requires vast amounts of memory in order to store all of the possible tracks. To avoid this complexity, more sophisticated single hypothesis data association techniques have been developed, such as the probabilistic data association filter (PDAF). These algorithms have enjoyed some success, but do not take advantage of any future data to help clarify ambiguous situations. On the other hand, the probabilistic multi-hypothesis tracking (PMHT) algorithm, proposed by Streit and Luginbuhl in 1995, attempts to use the best aspects of the MHT and the PDAF. In the PMHT algorithm, data is processed in batches, thereby using information from before and after each measurement to determine the likelihood of each measurement-to-track association. Furthermore, like the PDAF, it does not attempt to make hard assignments or enumerate all possible combinations, but instead associates each measurement with each track based upon its probability of association. Actual performance and initialization of the PMHT algorithm in the presence of significant clutter has not been adequately researched. This study focuses on the performance of the PMHT algorithm in dense clutter and the initialization thereof.

Download or read book Advanced Data Association Techniques in Multi target Tracking System written by Negm Eldin Mohamed Shawky and published by LAP Lambert Academic Publishing. This book was released on 2012 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: In multi-target tracking system, data association and tracking filter are two basic parts of tracking objects. The choosing of data association technique to associate the track to the true target in noisy received measurements is an important key to overcome the issues of the tracking process. Many data association algorithms have been developed to be the most powerful techniques for these issues, but still there are disadvantages in their restricting assumptions, complexity and in the resulting performance. For these reasons, some of data association algorithms that are widely used have been studied. These algorithms have some issues during tracking in dense clutter environment, tracking a highly maneuvering targets and swapping in the presence of more background clutter and false signal. Then, these algorithms have been updated to overcome the issues, improve the performance, decrease the burden of the computational cost, decrease the probability of error and to give the targets the ability to continue tracking without failing.

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 Algorithms for Tracking in Clutter and for Sensor Registration written by David Frederic Crouse and published by . This book was released on 2011 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Multiple target Tracking in Complex Scenarios written by Srinivas Phani Kumar Chavali and published by . This book was released on 2013 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, we develop computationally efficient algorithms for multiple-target tracking (MTT) in complex scenarios. For each of these scenarios, we develop measurement and state-space models, and then exploit the structure in these models to propose efficient tracking algorithms. In addition, we address design issues such as sensor selection and resource allocation. First, we consider MTT when the targets themselves are moving in a time-varying multipath environment. We develop a sparse-measurement model that allows us to exploit the inherent joint delay-Doppler diversity offered by the environment. We then reformulate the problem of MTT as a block-support recovery problem using the sparse measurement model. We exploit the structure of the dictionary matrix to develop a computationally efficient block support recovery algorithm (and thereby a multiple-target tracking algorithm) under the assumption that the channel state describing the time-varying multipath environment is known. Further, we also derive an upper bound on the overall error probability of wrongly identifying the support of the sparse signal. We then relax the assumption that the channel state is known. We develop a new particle filter called the Multiple Rao-Blackwellized Particle Filter (MRBPF) to jointly estimate both the target and the channel states. We also compute the posterior Cramér-Rao bound (PCRB) on the estimates of the target and the channel states and use the PCRB to find a suitable subset of antennas to be used for transmission in each tracking interval, as well as the power transmitted by these antennas. Second, we consider the problem of tracking an unknown number and types of targets using a multi-modal sensor network. In a multi-modal sensor network, different quantities associated with the same state are measured using sensors of different kinds. Hence, an efficient method that can suitably combine the diverse information measured by each sensor is required. We first develop a Hierarchical Particle Filter (HPF) to estimate the unknown state from the multi-modal measurements for a special class of problems which can be modeled hierarchically. We then model our problem of tracking using a hierarchical model and then use the proposed HPF for joint initiation, termination and tracking of multiple targets. The multi-modal data consists of the measurements collected from a radar, an infrared camera and a human scout. We also propose a unified framework for multi-modal sensor management that comprises sensor selection (SS), resource allocation (RA) and data fusion (DF). Our approach is inspired by the trading behavior of economic agents in commercial markets. We model the sensors and the sensor manager as economic agents, and the interaction among them as a double sided market with both consumers and producers. We propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of SS, RA and DF. Third, we address MTT problem in the presence of data association ambiguity that arises due to clutter. Data association corresponds to the problem of assigning a measurement to each target. We treat the data association and state estimation as separate subproblems. We develop a game-theoretic framework to solve the data association, in which we model each tracker as a player and the set of measurements as strategies. We develop utility functions for each player, and then use a regret-based learning algorithm to find the correlated equilibrium of this game. The game-theoretic approach allows us to associate measurements to all the targets simultaneously. We then use particle filtering on the reduced dimensional state of each target, independently.

Download or read book Multitarget Tracking Using Maximum Likelihood Techniques written by Wayne R. Blanding and published by . This book was released on 2007 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Maximum Likelihood-Probabilistic Data Association (ML-PDA) target tracking algorithm was originally developed for tracking Very Low Observable (VLO) or "dim" targets. VLO target tracking is challenging in that traditional Kalman Filter based tracking systems experience difficulty given the large quantity of clutter typically seen in measurement data sets. While effective, ML-PDA has not received wide acceptance as a target tracking algorithm because of its high computational complexity, the need for establishing a method for track validation, and its limitation to tracking single targets. This dissertation addresses each of these issues. First, two new computational methods are compared to the original method for computing the ML-PDA track estimate (Genetic Algorithm and Directed Subspace Search). We show that the Directed Subspace Search reduces the computational complexity of ML-PDA by an order of magnitude. Second, a new methodology for deriving the statistics required for track validation is presented which relies upon Extreme Value Theory (EVT). We show that the statistics of the ML-PDA Log Likelihood Ratio at the track estimate under the "target absent" hypothesis is most closely approximated by a Gumbel distribution and not the Gaussian distribution previously ascribed to it. We present two techniques for obtaining the track validation threshold, an off-line and a real-time technique, and demonstrate improved tracking performance through use of lower track validation threshold values. Third, we derive a version of ML-PDA for use in a multi-sensor problem. Fourth, we develop a multiple-target version of ML-PDA, called MLPDA(MT). MLPDA(MT) uses a multi-target version of the ML-PDA likelihood function for cases where measurements can be associated to multiple targets. Modules for track initiation, track maintenance/update, and track termination are also described. The effectiveness of each of these improvements to ML-PDA is tested through Monte Carlo simulations of target tracking problems and comparisons are made to either the baseline ML-PDA implementations or, in the case of MLPDA(MT), to the Probabilistic Multi-Hypothesis Tracker (PMHT). Simulation results show that by incorporating these innovations into ML-PDA, for the first time real-time target tracking is achievable without parallel processing. Further, ML-PDA(MT) performs better than PMHT in high clutter environments.

Download or read book Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems 3rd ICAUS 2023 written by Yi Qu and published by Springer Nature. This book was released on with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Radar Data Processing With Applications written by He You and published by John Wiley & Sons. This book was released on 2016-10-24 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: Radar Data Processing with Applications Radar Data Processing with Applications He You, Xiu Jianjuan, Guan Xin, Naval Aeronautical and Astronautical University, China A summary of thirty years’ worth of research, this book is a systematic introduction to the theory, development, and latest research results of radar data processing technology. Highlights of the book include sections on data pre-processing technology, track initiation, and data association. Readers are also introduced to maneuvering target tracking, multiple target tracking termination, and track management theory. In order to improve data analysis, the authors have also included group tracking registration algorithms and a performance evaluation of radar data processing. Presents both classical theory and development methods of radar data processing Provides state-of-the-art research results, including data processing for modern radars and tracking performance evaluation theory Includes coverage of performance evaluation, registration algorithm for radar networks, data processing of passive radar, pulse Doppler radar, and phased array radar Features applications for those engaged in information engineering, radar engineering, electronic countermeasures, infrared techniques, sonar techniques, and military command Radar Data Processing with Applications is a handy guide for engineers and industry professionals specializing in the development of radar equipment and data processing. It is also intended as a reference text for electrical engineering graduate students and researchers specializing in signal processing and radars.

Download or read book Tracking and Sensor Data Fusion written by Wolfgang Koch and published by Springer Science & Business Media. This book was released on 2013-09-20 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensor Data Fusion is the process of combining incomplete and imperfect pieces of mutually complementary sensor information in such a way that a better understanding of an underlying real-world phenomenon is achieved. Typically, this insight is either unobtainable otherwise or a fusion result exceeds what can be produced from a single sensor output in accuracy, reliability, or cost. This book provides an introduction Sensor Data Fusion, as an information technology as well as a branch of engineering science and informatics. Part I presents a coherent methodological framework, thus providing the prerequisites for discussing selected applications in Part II of the book. The presentation mirrors the author's views on the subject and emphasizes his own contributions to the development of particular aspects. With some delay, Sensor Data Fusion is likely to develop along lines similar to the evolution of another modern key technology whose origin is in the military domain, the Internet. It is the author's firm conviction that until now, scientists and engineers have only scratched the surface of the vast range of opportunities for research, engineering, and product development that still waits to be explored: the Internet of the Sensors.