Download or read book Passive Target Tracking Using Nonlinear Estimation Theory written by Marcilio Boavista Da Cunha and published by . This book was released on 2003 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of tracking submarine targets with passive sonobuoys is mathematically modelled. A comprehensive study is made of all the information available in the acoustic signals picked up by the sonobuoys and of the usefulness of this information in the estimation process. The presence of nonlinearities in the tracking model leads to the application of nonlinear estimation theory. Bayes formulation concepts are applied to generate approximate solutions and filtering algorithms, and the well known extended Kalman filter equations and higher order filtering algorithms are obtained from this approach. The concept of partitioning the measurements is presented and shown to bring advantages in computing efficiency and also, for nonlinear measurements, in tracking accuracy. A graphical interpretation of the action of Kalman filters is developed and provides insight into the importance of each variable in the filtering process. Extensive simulations, designed to test the performance of the algorithms developed, are presented in graphical form and analyzed. (Author).
Download or read book Passive Target Tracking Using Nonlinear Estimation Theory written by Marcilio Boavista da Cunha and published by . This book was released on 1976 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of tracking submarine targets with passive sonobuoys is mathematically modelled. A comprehensive study is made of all the information available in the acoustic signals picked up by the sonobuoys and of the usefulness of this information in the estimation process. The presence of nonlinearities in the tracking model leads to the application of nonlinear estimation theory. Bayes formulation concepts are applied to generate approximate solutions and filtering algorithms, and the well known extended Kalman filter equations and higher order filtering algorithms are obtained from this approach. The concept of partitioning the measurements is presented and shown to bring advantages in computing efficiency and also, for nonlinear measurements, in tracking accuracy. A graphical interpretation of the action of Kalman filters is developed and provides insight into the importance of each variable in the filtering process. Extensive simulations, designed to test the performance of the algorithms developed, are presented in graphical form and analyzed. (Author).
Download or read book Nonlinear Estimation written by Shovan Bhaumik and published by CRC Press. This book was released on 2019-07-24 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonlinear Estimation: Methods and Applications with Deterministic Sample Points focusses on a comprehensive treatment of deterministic sample point filters (also called Gaussian filters) and their variants for nonlinear estimation problems, for which no closed-form solution is available in general. Gaussian filters are becoming popular with the designers due to their ease of implementation and real time execution even on inexpensive or legacy hardware. The main purpose of the book is to educate the reader about a variety of available nonlinear estimation methods so that the reader can choose the right method for a real life problem, adapt or modify it where necessary and implement it. The book can also serve as a core graduate text for a course on state estimation. The book starts from the basic conceptual solution of a nonlinear estimation problem and provides an in depth coverage of (i) various Gaussian filters such as the unscented Kalman filter, cubature and quadrature based filters, Gauss-Hermite filter and their variants and (ii) Gaussian sum filter, in both discrete and continuous-discrete domain. Further, a brief description of filters for randomly delayed measurement and two case-studies are also included. Features: The book covers all the important Gaussian filters, including filters with randomly delayed measurements. Numerical simulation examples with detailed matlab code are provided for most algorithms so that beginners can verify their understanding. Two real world case studies are included: (i) underwater passive target tracking, (ii) ballistic target tracking. The style of writing is suitable for engineers and scientists. The material of the book is presented with the emphasis on key ideas, underlying assumptions, algorithms, and properties. The book combines rigorous mathematical treatment with matlab code, algorithm listings, flow charts and detailed case studies to deepen understanding.
Download or read book An Application of Nonlinear Filtering Theory to Passive Target Location and Tracking written by George William Mitschang and published by . This book was released on 1974 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The technique of Extended Kalman filtering is applied to a passive single sensor location and tracking problem. Signal angle of arrival and doppler shifted frequency are used as observations for two different filter formulations: one for fixed X-Y coordinates of position and velocity and one with an orthogonal system rotated to align an axis with the target heading. The fixed X-Y system was found to give better tracking performance on the Monte Carlo simulation. A new method of calculating the initial covariance matrix was extended to reformulate the Kalman filter equations for a nonlinear problem. (Modified author abstract).
Download or read book Nonlinear Estimation with Applications to Target Tracking written by Robert Louis Bellaire and published by . This book was released on 1996 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Passive Sensor Bias Estimation Using Targets of Opportunity written by and published by . This book was released on 2015 with total page 280 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 Nonlinear Estimation Techniques for Target Tracking written by Shaun Joseph McGinnity and published by . This book was released on 1998 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Beyond the Kalman Filter Particle Filters for Tracking Applications written by Branko Ristic and published by Artech House. This book was released on 2003-12-01 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: For most tracking applications the Kalman filter is reliable and efficient, but it is limited to a relatively restricted class of linear Gaussian problems. To solve problems beyond this restricted class, particle filters are proving to be dependable methods for stochastic dynamic estimation. Packed with 867 equations, this cutting-edge book introduces the latest advances in particle filter theory, discusses their relevance to defense surveillance systems, and examines defense-related applications of particle filters to nonlinear and non-Gaussian problems. With this hands-on guide, you can develop more accurate and reliable nonlinear filter designs and more precisely predict the performance of these designs. You can also apply particle filters to tracking a ballistic object, detection and tracking of stealthy targets, tracking through the blind Doppler zone, bi-static radar tracking, passive ranging (bearings-only tracking) of maneuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and extended object tracking.
Download or read book Technical Abstract Bulletin written by and published by . This book was released on with total page 828 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Passive Underwater Target Tracking Using Bearings only Measurements written by Sanagapallea Koteswara Rao and published by LAP Lambert Academic Publishing. This book was released on 2012-06 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, passive target tracking using bearings-only measurements is studied for several underwater applications. For submarine (ownship) to submarine (target) application, Pseudo Linear Estimator and its variants are developed for various situations. The algorithm is extended to Electronic Surveillance Measures in Electronic Warfare / Intercept sonar target tracking application, where the measurements are highly aperiodic. In underwater, prior information of the target motion parameters will not be available. Therefore, Pseudo Linear Estimator is developed in such a way to work without initialization of target state vector. As this does not require statistical characteristics of noise, this can be safely used as a back up algorithm, along with any variant of Kalman filter.
Download or read book Werneuchen written by and published by . This book was released on 1882 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Rami dan kemungkinan2 serta harapan2 nja di Indonesia written by and published by . This book was released on 1952 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Underwater Target Tracking Using Measurements from a Linear Array of Omnidirectional Acoustic Elements written by NAVAL UNDERWATER SYSTEMS CENTER NEWPORT RI. and published by . This book was released on 1981 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tracking of underwater targets using measurements from a geometrically linear array of omnidirectional acoustic elements is a nonlinear estimation problem. The technique constructed and used in this report is an iterative, least-squares algorithm which uses the modified Gauss-Newton method. In this algorithm the search direction is found by applying a Householder transformation to the Gauss-Newton equations; the size of the step in this direction is found by minimizing a polynomial approximation to the performance index. The results show: the sign on the estimate of the target's depth cannot be uniquely determined, the Gauss-Newton method works as well as its modified version for these experiments, the problem is singular on the first leg, and the initialization of the algorithm influences convergence. (Author).
Download or read book Bayesian Bounds for Parameter Estimation and Nonlinear Filtering Tracking written by Harry L. Van Trees and published by Wiley-IEEE Press. This book was released on 2007-08-31 with total page 951 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first comprehensive development of Bayesian Bounds for parameter estimation and nonlinear filtering/tracking Bayesian estimation plays a central role in many signal processing problems encountered in radar, sonar, communications, seismology, and medical diagnosis. There are often highly nonlinear problems for which analytic evaluation of the exact performance is intractable. A widely used technique is to find bounds on the performance of any estimator and compare the performance of various estimators to these bounds. This book provides a comprehensive overview of the state of the art in Bayesian Bounds. It addresses two related problems: the estimation of multiple parameters based on noisy measurements and the estimation of random processes, either continuous or discrete, based on noisy measurements. An extensive introductory chapter provides an overview of Bayesian estimation and the interrelationship and applicability of the various Bayesian Bounds for both static parameters and random processes. It provides the context for the collection of papers that are included. This book will serve as a comprehensive reference for engineers and statisticians interested in both theory and application. It is also suitable as a text for a graduate seminar or as a supplementary reference for an estimation theory course.
Download or read book Optimal State Estimation written by Dan Simon and published by John Wiley & Sons. This book was released on 2006-06-19 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: A bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering. While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning: * Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation * Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice * MATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering. Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.
Download or read book Bayesian Estimation and Tracking written by Anton J. Haug and published by John Wiley & Sons. This book was released on 2012-05-29 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation of all tracking algorithms within a Bayesian framework and describes effective numerical methods for evaluating density-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The author first emphasizes detailed derivations from first principles of eeach estimation method and goes on to use illustrative and detailed step-by-step instructions for each method that makes coding of the tracking filter simple and easy to understand. Case studies are employed to showcase applications of the discussed topics. In addition, the book supplies block diagrams for each algorithm, allowing readers to develop their own MATLAB® toolbox of estimation methods. Bayesian Estimation and Tracking is an excellent book for courses on estimation and tracking methods at the graduate level. The book also serves as a valuable reference for research scientists, mathematicians, and engineers seeking a deeper understanding of the topics.
