Download or read book Computational Methods for Probabilistic Target Tracking Problems written by and published by . This book was released on 2007 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: The grant started in 2003. Initially a cohort of two graduate students and four sophomore undergraduate students was recruited. The students received special training in probabilistic and statistical methods pertaining to target -tracking problems. Particular topics included Kalman filtering, the EM Algorithm, smoothing methods, and density estimation. In the summer of 2004 the graduate students accompanied Dr. Warrack to The Naval Undersea Warfare Center, Newport, RI (NUWC-Newport), for a 10 week internship working under the supervision of Dr Roy Streit. This resulted in a presentation at NUWC, Applying Density Estimation and Nonparametric Smoothing Techniques to Tracking Problems. In the summer of 2005 the undergraduates accompanied Dr. Warrack to NUWC-Newport for a 10 week internship under the direction of Dr. Marcus Graham. A presentation Using Parametric and Nonparametric Smoothing Techniques to Improve Estimation with the EM Algorithm was given at NUWC. All six of the students have graduated with high grade point averages. Three received NAVSEA job offers, one of whom is working at NSWCDD-Dahlgren, VA. During an extension year two graduate students and two undergraduates were supported.

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 Probabilistic Search for Tracking Targets written by Irad Ben-Gal and published by John Wiley & Sons. This book was released on 2013-03-25 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a probabilistic and information-theoretic framework for a search for static or moving targets in discrete time and space. Probabilistic Search for Tracking Targets uses an information-theoretic scheme to present a unified approach for known search methods to allow the development of new algorithms of search. The book addresses search methods under different constraints and assumptions, such as search uncertainty under incomplete information, probabilistic search scheme, observation errors, group testing, search games, distribution of search efforts, single and multiple targets and search agents, as well as online or offline search schemes. The proposed approach is associated with path planning techniques, optimal search algorithms, Markov decision models, decision trees, stochastic local search, artificial intelligence and heuristic information-seeking methods. Furthermore, this book presents novel methods of search for static and moving targets along with practical algorithms of partitioning and search and screening. Probabilistic Search for Tracking Targets includes complete material for undergraduate and graduate courses in modern applications of probabilistic search, decision-making and group testing, and provides several directions for further research in the search theory. The authors: Provide a generalized information-theoretic approach to the problem of real-time search for both static and moving targets over a discrete space. Present a theoretical framework, which covers known information-theoretic algorithms of search, and forms a basis for development and analysis of different algorithms of search over probabilistic space. Use numerous examples of group testing, search and path planning algorithms to illustrate direct implementation in the form of running routines. Consider a relation of the suggested approach with known search theories and methods such as search and screening theory, search games, Markov decision process models of search, data mining methods, coding theory and decision trees. Discuss relevant search applications, such as quality-control search for nonconforming units in a batch or a military search for a hidden target. Provide an accompanying website featuring the algorithms discussed throughout the book, along with practical implementations procedures.

Download or read book Stochastic Algorithms for Visual Tracking written by John MacCormick and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: A central problem in computer vision is to track objects as they move and deform in a video sequence. Stochastic algorithms -- in particular, particle filters and the Condensation algorithm -- have dramatically enhanced the state of the art for such visual tracking problems in recent years. This book presents a unified framework for visual tracking using particle filters, including the new technique of partitioned sampling which can alleviate the "curse of dimensionality" suffered by standard particle filters. The book also introduces the notion of contour likelihood: a collection of models for assessing object shape, colour and motion, which are derived from the statistical properties of image features. Because of their statistical nature, contour likelihoods are ideal for use in stochastic algorithms. A unifying theme of the book is the use of statistics and probability, which enable the final output of the algorithms presented to be interpreted as the computer's "belief" about the state of the world. The book will be of use and interest to students, researchers and practitioners in computer vision, and assumes only an elementary knowledge of probability theory.

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 Sensor Management for Target Tracking Applications written by Per Boström-Rost and published by Linköping University Electronic Press. This book was released on 2021-04-12 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many practical applications, such as search and rescue operations and environmental monitoring, involve the use of mobile sensor platforms. The workload of the sensor operators is becoming overwhelming, as both the number of sensors and their complexity are increasing. This thesis addresses the problem of automating sensor systems to support the operators. This is often referred to as sensor management. By planning trajectories for the sensor platforms and exploiting sensor characteristics, the accuracy of the resulting state estimates can be improved. The considered sensor management problems are formulated in the framework of stochastic optimal control, where prior knowledge, sensor models, and environment models can be incorporated. The core challenge lies in making decisions based on the predicted utility of future measurements. In the special case of linear Gaussian measurement and motion models, the estimation performance is independent of the actual measurements. This reduces the problem of computing sensing trajectories to a deterministic optimal control problem, for which standard numerical optimization techniques can be applied. A theorem is formulated 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 globally optimal sensing trajectories can be computed using off-the-shelf optimization tools. As in many other fields, nonlinearities make sensor management problems more complicated. Two approaches are derived to handle the randomness inherent in the nonlinear problem of tracking a maneuvering target using a mobile range-bearing sensor with limited field of view. The first approach uses deterministic sampling to predict several candidates of future target trajectories that are taken into account when planning the sensing trajectory. This significantly increases the tracking performance compared to a conventional approach that neglects the uncertainty in the future target trajectory. The second approach is a method to find the optimal range between the sensor and the target. Given the size of the sensor's field of view and an assumption of the maximum acceleration of the target, the optimal range is determined as the one that minimizes the tracking error while satisfying a user-defined constraint on the probability of losing track of the target. While optimization for tracking of a single target may be difficult, planning for jointly maintaining track of discovered targets and searching for yet undetected targets is even more challenging. Conventional approaches are typically based on a traditional tracking method with separate handling of undetected targets. Here, it is shown that the Poisson multi-Bernoulli mixture (PMBM) filter provides a theoretical foundation for a unified search and track method, as it not only provides state estimates of discovered targets, but also maintains an explicit representation of where undetected targets may be located. Furthermore, in an effort to decrease the computational complexity, a version of the PMBM filter which uses a grid-based intensity to represent undetected targets is derived.

Download or read book Feature Based Probabilistic Data Association for Video Based Multi Object Tracking written by Grinberg, Michael and published by KIT Scientific Publishing. This book was released on 2018-08-10 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work proposes a feature-based probabilistic data association and tracking approach (FBPDATA) for multi-object tracking. FBPDATA is based on re-identification and tracking of individual video image points (feature points) and aims at solving the problems of partial, split (fragmented), bloated or missed detections, which are due to sensory or algorithmic restrictions, limited field of view of the sensors, as well as occlusion situations.

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.

Download or read book Probabilistic Robotics written by Sebastian Thrun and published by MIT Press. This book was released on 2005-08-19 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

Download or read book Analytic Combinatorics for Multiple Object Tracking written by Roy Streit and published by Springer Nature. This book was released on 2020-11-26 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​The book shows that the analytic combinatorics (AC) method encodes the combinatorial problems of multiple object tracking—without information loss—into the derivatives of a generating function (GF). The book lays out an easy-to-follow path from theory to practice and includes salient AC application examples. Since GFs are not widely utilized amongst the tracking community, the book takes the reader from the basics of the subject to applications of theory starting from the simplest problem of single object tracking, and advancing chapter by chapter to more challenging multi-object tracking problems. Many established tracking filters (e.g., Bayes-Markov, PDA, JPDA, IPDA, JIPDA, CPHD, PHD, multi-Bernoulli, MBM, LMBM, and MHT) are derived in this manner with simplicity, economy, and considerable clarity. The AC method gives significant and fresh insights into the modeling assumptions of these filters and, thereby, also shows the potential utility of various approximation methods that are well established techniques in applied mathematics and physics, but are new to tracking. These unexplored possibilities are reviewed in the final chapter of the book.

Download or read book Estimation and Tracking written by Yaakov Bar-Shalom and published by Ybs Pub. This book was released on 1998 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 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 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.

Download or read book Handbook of Probabilistic Models written by Pijush Samui and published by Butterworth-Heinemann. This book was released on 2019-10-05 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more. - Explains the application of advanced probabilistic models encompassing multidisciplinary research - Applies probabilistic modeling to emerging areas in engineering - Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems

Download or read book IEEE TENCON 2003 written by and published by Allied Publishers. This book was released on 2003 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computational Methods for Deep Learning written by Wei Qi Yan and published by Springer Nature. This book was released on 2023-10-17 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book. The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.