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 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 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 Nonlinear State Estimation and Filtering with Applications to Target Tracking Problems written by Subhash Challa and published by . This book was released on 1998 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 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 0 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 Grid based Nonlinear Estimation and Its Applications written by Bin Jia and published by CRC Press. This book was released on 2019-04-25 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grid-based Nonlinear Estimation and its Applications presents new Bayesian nonlinear estimation techniques developed in the last two decades. Grid-based estimation techniques are based on efficient and precise numerical integration rules to improve performance of the traditional Kalman filtering based estimation for nonlinear and uncertainty dynamic systems. The unscented Kalman filter, Gauss-Hermite quadrature filter, cubature Kalman filter, sparse-grid quadrature filter, and many other numerical grid-based filtering techniques have been introduced and compared in this book. Theoretical analysis and numerical simulations are provided to show the relationships and distinct features of different estimation techniques. To assist the exposition of the filtering concept, preliminary mathematical review is provided. In addition, rather than merely considering the single sensor estimation, multiple sensor estimation, including the centralized and decentralized estimation, is included. Different decentralized estimation strategies, including consensus, diffusion, and covariance intersection, are investigated. Diverse engineering applications, such as uncertainty propagation, target tracking, guidance, navigation, and control, are presented to illustrate the performance of different grid-based estimation techniques.
Download or read book Grid based Nonlinear Estimation and Its Applications written by Bin Jia and published by CRC Press. This book was released on 2019-04-25 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grid-based Nonlinear Estimation and its Applications presents new Bayesian nonlinear estimation techniques developed in the last two decades. Grid-based estimation techniques are based on efficient and precise numerical integration rules to improve performance of the traditional Kalman filtering based estimation for nonlinear and uncertainty dynamic systems. The unscented Kalman filter, Gauss-Hermite quadrature filter, cubature Kalman filter, sparse-grid quadrature filter, and many other numerical grid-based filtering techniques have been introduced and compared in this book. Theoretical analysis and numerical simulations are provided to show the relationships and distinct features of different estimation techniques. To assist the exposition of the filtering concept, preliminary mathematical review is provided. In addition, rather than merely considering the single sensor estimation, multiple sensor estimation, including the centralized and decentralized estimation, is included. Different decentralized estimation strategies, including consensus, diffusion, and covariance intersection, are investigated. Diverse engineering applications, such as uncertainty propagation, target tracking, guidance, navigation, and control, are presented to illustrate the performance of different grid-based estimation techniques.
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 197 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 Adaptive Estimation for Control of Uncertain Nonlinear Systems with Applications to Target Tracking written by Venkatesh Madyastha and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Design of nonlinear observers has received considerable attention since the early development of methods for linear state estimation. The most popular approach is the extended Kalman filter (EKF), that goes through significant degradation in the presence of nonlinearities, particularly if unmodeled dynamics are coupled to the process and the measurement. For uncertain nonlinear systems, adaptive observers have been introduced to estimate the unknown state variables where no priori information about the unknown parameters is available. While establishing global results, these approaches are applicable only to systems transformable to output feedback form. Over the recent years, neural network (NN) based identification and estimation schemes have been proposed that relax the assumptions on the system at the price of sacrificing on the global nature of the results. However, most of the NN based adaptive observer approaches in the literature require knowledge of the full dimension of the system, therefore may not be suitable for systems with unmodeled dynamics. We first propose a novel approach to nonlinear state estimation from the perspective of augmenting a linear time invariant observer with an adaptive element. The class of nonlinear systems treated here are finite but of otherwise unknown dimension. The objective is to improve the performance of the linear observer when applied to a nonlinear system. The approach relies on the ability of the NNs to approximate the unknown dynamics from finite time histories of available measurements. Next we investigate nonlinear state estimation from the perspective of adaptively augmenting an existing time varying observer, such as an EKF. EKFs find their applications mostly in target tracking problems. The proposed approaches are robust to unmodeled dynamics, including unmodeled disturbances. Lastly, we consider the problem of adaptive estimation in the presence of feedback control for a class of uncertain nonlinear systems with unmodeled dynamics and disturbances coupled to the process. The states from the adaptive EKF are used as inputs to the control law, which in target tracking usually takes the form of a guidance law. The applications of this approach lie in the areas of missile-target tracking, formation flight control and obstacle avoidance.
Download or read book Nonlinear Estimation for Vision based Air to air Tracking written by Seung-Min Oh and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Unmanned aerial vehicles (UAV's) have been the focus of significant research interest in both military and commercial areas since they have a variety of practical applications including reconnaissance, surveillance, target acquisition, search and rescue, patrolling, real-time monitoring, and mapping, to name a few. To increase the autonomy and the capability of these UAV's and thus to reduce the workload of human operators, typical autonomous UAV's are usually equipped with both a navigation system and a tracking system. The navigation system provides high-rate ownship states (typically ownship inertial position, inertial velocity, and attitude) that are directly used in the autopilot system, and the tracking system provides low-rate target tracking states (typically target relative position and velocity with respect to the ownship). Target states in the global frame can be obtained by adding the ownship states and the target tracking states. The data estimated from this combination of the navigation system and the tracking system provide key information for the design of most UAV guidance laws, control command generation, trajectory generation, and path planning.
Download or read book Locating Classifying and Countering Agile Land Vehicles written by David D. Sworder and published by Springer. This book was released on 2015-07-28 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines real-time target tracking and identification algorithms with a focus on tracking an agile target. The authors look at several problems in which the tradeoff of accuracy and confidence must be made. These issues are explored within the context of specific tracking scenarios chosen to illustrate the tradeoffs in a simple and direct manner. The text covers the Gaussian wavelet estimator (GWE) which has a flexible architecture that is able to fuse uncommon sensor combinations with non-temporal structural constraints.
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 Application and Performance Comparison of Recent Advances in Nonlinear State Estimation to Tracking a Ballistic Missile Target written by Brian G. Saulson and published by . This book was released on 2002 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 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.
