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 Evaluation and Extensions of the Probabilistic Multi Hypothesis Tracking Algorithm to Cluttered Environments written by Robert G. Hutchins and published by . This book was released on 1998 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research examines the probabilistic multi-hypothesis tracker (PHMT), a batch mode, empirical, Bayesian data association and tracking algorithm. Like a traditional multi-hypothesis tracker (MHT), track estimation is deferred until more conclusive data is gathered. However, unlike a traditional algorithm, PMHT does not attempt to enumerate all possible combinations of feasible data association links, but uses a probabilistic structure derived using expectation maximization. This study focuses on two issues: the behavior of the PMHT algorithm in clutter and algorithm initialization in clutter. We also compare performance between this algorithm and other algorithms, including a nearest neighbor tracker, a probabilistic data association filter (PDAF), and a traditional measurement oriented MHT algorithm.

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 A Multiple Hypothesis Filter for Tracking Multiple Targets in a Cluttered Environment written by Donald B. Reid and published by . This book was released on 1977 with total page 79 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 Track Before Detect Using Expectation Maximisation written by Samuel J. Davey and published by Springer. This book was released on 2018-02-08 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a detailed description of the histogram probabilistic multi-hypothesis tracker (H-PMHT), providing an accessible and intuitive introduction to the mathematical mechanics of H-PMHT as well as a definitive reference source for the existing literature on the method. Beginning with basic concepts, the authors then move on to address extensions of the method to a broad class of tracking problems. The latter chapters present applications using recorded data from experimental radar, sonar and video sensor systems.

Download or read book Extensions to the Probabilistic Multi hypothesis Tracker for Tracking Navigation and SLAM written by Brian Cheung and published by . This book was released on 2012 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-target tracking is a problem that involves estimating target states from noisy data whilst simultaneously deciding which measurement was produced by each target. The Probabilistic Multi-Hypothesis Tracker (PMHT) is an algorithm that solves the multi-target tracking problem. This thesis presents extensions to the PMHT to address problems that may arise in the use of real sensors and considers multi-target tracking techniques for use in other applications such as autonomous vehicles. It is generally assumed that a sensor collects a set of noisy position measurements at known times. In some situations, the time information may not be reliable and cause filtering issues. This thesis derives an extension to the PMHT that introduces an assignment index that identifies the true time at which a measurement was collected. This extension of the PMHT allows for tracking on measurements with time errors, such as time delays. A further extension allows the PMHT algorithm to simultaneously estimate the time error parameters whilst tracking targets. The above extension is applied to the problem of planning paths for multiple platforms to explore an unknown area. Given a set of locales to be visited and the platform initial positions, the path planning problem has the same mathematical form as a multi-target tracking problem, with locales as measurements and the platforms as targets. The extended PMHT algorithm uses hypothesised time-stamps to associate locales to platforms and times simultaneously. Autonomous vehicles are expected to use information from their sensors to navigate and map their environment. Simultaneous localisation and mapping (SLAM) is the name given to this task and is essentially a multi-target tracking problem. This thesis proposes the use of PMHT and landmark classification information received with measurements to improve the performance of SLAM.

Download or read book Design and Analysis of Modern Tracking Systems written by Samuel S. Blackman and published by Artech House Publishers. This book was released on 1999 with total page 1306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Here's a thorough overview of the state-of-the-art in design and implementation of advanced tracking for single and multiple sensor systems. This practical resource provides modern system designers and analysts with in-depth evaluations of sensor management, kinematic and attribute data processing, data association, situation assessment, and modern tracking and data fusion methods as applied in both military and non-military arenas.

Download or read book A COMPARISON OF THE PROBABILITY HYPOTHESIS DENSITY FILTER AND THE MULTIPLE HYPOTHESIS TRACKER FOR TRACKING TARGETS OF MULTIPLE TYPES written by James A. Brodovsky and published by . This book was released on 2019 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robotic technology is advancing out of the laboratory and into the everyday world. This world is less ordered than the laboratory and requires an increased ability to identify, target, and track objects of importance. The Bayes filter is the ideal algorithm for tracking a single target and there exists a significant body of work detailing tractable approximations of it with the notable examples of the Kalman and Extended Kalman filter. Multiple target tracking also relies on a similar principle and the Kalman and Extended Kalman filter have multi-target implementations as well. Other method include the PHD filter and Multiple Hypothesis tracker. One issue is that these methods were formulated to only track one classification of target. With the increased need for robust perception, there exists a need to develop a target tracking algorithm that is capable of identifying and tracking targets of multiple classifications. This thesis examines two of these methods: the Probability Hypothesis Density (PHD) filter and the Multiple Hypothesis Tracker (MHT). A Matlab-based simulation of an office floor plan is developed and a simulation UGV equipped with a camera is set the task of navigating the floor plan and identifying targets. Results of these experiments indicated that both methods are mathematically capable of achieving this. However, there was a significant reliance on post-processing to verify the performance of each algorithm and filter out noisy sensor inputs indicating that specific multi-target multi-class implementations of each algorithm should be implemented with a detailed and more accurate sensor model.

Download or read book Urban Terrain Multiple Target Tracking Using the Probability Hypothesis Density Particle Filter written by Meng Zhou and published by . This book was released on 2011 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: The tracking of multiple targets becomes more challenging in complex environments due to the additional degrees of nonlinearity in the measurement model. In urban terrain, for example, there are multiple reflection path measurements that need to be exploited since line-of-sight observations are not always available. Multiple target tracking in urban terrain environments is traditionally implemented using sequential Monte Carlo filtering algorithms and data association techniques. However, data association techniques can be computationally intensive and require very strict conditions for efficient performance.

Download or read book Radar Data Processing With Applications written by He You and published by John Wiley & Sons. This book was released on 2016-08-01 with total page 558 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 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 Estimation with Applications to Tracking and Navigation written by Yaakov Bar-Shalom and published by John Wiley & Sons. This book was released on 2004-04-05 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expert coverage of the design and implementation of state estimation algorithms for tracking and navigation Estimation with Applications to Tracking and Navigation treats the estimation of various quantities from inherently inaccurate remote observations. It explains state estimator design using a balanced combination of linear systems, probability, and statistics. The authors provide a review of the necessary background mathematical techniques and offer an overview of the basic concepts in estimation. They then provide detailed treatments of all the major issues in estimation with a focus on applying these techniques to real systems. Other features include: * Problems that apply theoretical material to real-world applications * In-depth coverage of the Interacting Multiple Model (IMM) estimator * Companion DynaEst(TM) software for MATLAB(TM) implementation of Kalman filters and IMM estimators * Design guidelines for tracking filters Suitable for graduate engineering students and engineers working in remote sensors and tracking, Estimation with Applications to Tracking and Navigation provides expert coverage of this important area.

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 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 Probabilistic Multi Hypothesis Tracking written by and published by . This book was released on 1995 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a multitarget, multimeasurement environment, knowledge of the measurement-to-track assignments is typically unavailable to the tracking algorithm. This study is a probabilistic approach to the measurement-to-track assignment problem. Measurements are not assigned to tracks as in traditional multi-hypothesis tracking (MHT) algorithms; Instead, the probability that each measurement belongs to each track is estimated using a maximum a posteriori (MAP) method. These measurement-to-track probability estimates are intrinsic to the multitarget tracker called the probabilistic multi-hypothesis tracking (PMHT) algorithm. The PMHT algorithm is computationally practical because it requires neither enumeration of measurement-to-track assignments nor pruning. The PMHT algorithm is an optimal MAP multitarget tracking algorithm. (AN).

Download or read book Multiple Target Tracking and Data Fusion Via Probabilistic Mapping written by and published by . This book was released on 2000 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new approach is taken to address the various aspects of the multi-sensor, multi-target tracking (MTT) problem in dense and noisy environments. Instead of fixing the trackers on the potential targets as the conventional tracking algorithms do, this new approach is fundamentally different in that an array of parallel-distributed trackers is laid in the search space. The difficult data-track association problem that has challenged the conventional trackers becomes a nonissue with this new approach. By partitioning the search space into cells, this new approach, called PMAP (probabilistic mapping), dynamically calculates the spatial probability distribution of targets in the search space via Bayesian updates. The distribution is spread at each time step, following a fairly general Markov-chain target motion model, to become the prior probabilities of the next scan. This framework can effectively handle data from multiple sensors and incorporate contextual information, such as terrain and weather, by performing a form of evidential reasoning. Used as a pre-filtering device, the PMAP is shown to remove noiselike false alarms effectively, while keeping the target dropout rate very low. This gives the downstream track linker a much easier job to perform. A related benefit is that with PMAP it is now possible to lower the detection threshold and to enjoy high probability of detection and low probability of false alarm at the same time, thereby improving overall tracking performance. The feasibility of using PMAP to track specific targets in an end-game scenario is also demonstrated. Both real and simulated data are used to illustrate the PMAP performance. The PMAP algorithm is parallel distributed in nature; for serial computer implementation, fast algorithms have been developed. Some related applications based on the PMAP approach, including a spatial-temporal sensor data fusion application and a gray-scale video sequence stacking application, are also discussed.