Adaptive Filter Theory (5th Edition)
Adaptive Filter Theory, 5e, is ideal for courses in Adaptive Filters.
Haykin examines both the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fifth edition, this highly successful book has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible.
Fundamentals of Kalman Filtering (Progress in Aeronautics and Astronautics)
In 2008 the National Academy of Engineering awarded Rudolf Kalman the Charles Stark Draper Prize--the engineering equivalent of the Nobel Prize -- for the development and dissemination of the optimal digital technique (known as the Kalman Filter) that is pervasively used to control a vast array of consumer, health, commercial, and defense products. Fundamentals of Kalman Filtering, Fourth Edition is a practical guide to building Kalman filters that shows how the filtering equations can be...
Fundamentals of Kalman Filtering (Progress in Astronautics and Aeronautics)
This is a practical guide to building Kalman filters that shows how the filtering equations can be applied to real-life problems. Numerous examples are presented in detail, showing the many ways in which Kalman filters can be designed. Computer code written in FORTRAN, MATLAB[registered], and True BASIC accompanies all of the examples so that the interested reader can verify concepts and explore issues beyond the scope of the text. In certain instances, the authors intentionally introduce...
Kalman Filtering: with Real-Time Applications
This new edition presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear...
Linear Estimation
This original work offers the most comprehensive and up-to-date treatment of the important subject of optimal linear estimation, which is encountered in many areas of engineering such as communications, control, and signal processing, and also several other fields, e.g., econometrics and statistics. The book not only highlights the most significant contributions to this field during the 20th century, including the works of Weiner and Kalman, but it does so in an original and novel manner...
Estimation with Applications to Tracking and Navigation
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...
Kalman Filtering: Theory and Practice with MATLAB
The definitive textbook and professional reference on Kalman Filtering fully updated, revised, and expanded
This book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for...
A Kalman Filter Primer (Statistics: A Series of Textbooks and Monographs)
System state estimation in the presence of noise is critical for control systems, signal processing, and many other applications in a variety of fields. Developed decades ago, the Kalman filter remains an important, powerful tool for estimating the variables in a system in the presence of noise. However, when inundated with theory and vast notations, learning just how the Kalman filter works can be a daunting task.
With its mathematically rigorous, “no frills” approach to the basic...
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
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...
Beyond the Kalman Filter: Particle Filters for Tracking Applications (Artech House Radar Library)
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...
Digital and Kalman Filtering: An Introduction to Discrete-Time Filtering and Optimum Linear Estimation, Second Edition (
This text for advanced undergraduates and graduate students provides a concise introduction to increasingly important topics in electrical engineering: digital filtering, filter design, and applications in the form of the Kalman and Wiener filters. The first half focuses on digital filtering, covering FIR and IIR filter design and other concepts. The second half addresses filtering noisy data to extract a signal, with chapters on nonrecursive (FIR Wiener) estimation, recursive (Kalman)...
Kalman Filtering: with Real-Time Applications
This new edition presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear...
Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods (Adaptive and Cognitive Dynamic Systems: S
Presents the Bayesian approach to statistical signal processing for a variety of useful model sets
This book aims to give readers a unified Bayesian treatment starting from the basics (Baye’s rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on “Sequential Bayesian Detection,” a new section on “Ensemble Kalman Filters” as well as an expansion of...
Fundamentals of Kalman Filtering (Progress in Aeronautics and Astronautics)
In 2008 the National Academy of Engineering awarded Rudolf Kalman the Charles Stark Draper Prize--the engineering equivalent of the Nobel Prize -- for the development and dissemination of the optimal digital technique (known as the Kalman Filter) that is pervasively used to control a vast array of consumer, health, commercial, and defense products. Fundamentals of Kalman Filtering, Fourth Edition is a practical guide to building Kalman filters that shows how the filtering equations can be...
Random Processes for Engineers
This engaging introduction to random processes provides students with the critical tools needed to design and evaluate engineering systems that must operate reliably in uncertain environments. A brief review of probability theory and real analysis of deterministic functions sets the stage for understanding random processes, whilst the underlying measure theoretic notions are explained in an intuitive, straightforward style. Students will learn to manage the complexity of randomness through...
Bayesian Multiple Target Tracking, Second Edition
This second edition has undergone substantial revision from the 1999 first edition, recognizing that a lot has changed in the multiple target tracking field. One of the most dramatic changes is in the widespread use of particle filters to implement nonlinear, non-Gaussian Bayesian trackers. This book views multiple target tracking as a Bayesian inference problem. Within this framework it develops the theory of single target tracking. In addition to providing a detailed description of a basic...
Kalman Filtering: Theory and Practice with MATLAB
The definitive textbook and professional reference on Kalman Filtering fully updated, revised, and expanded
This book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for...
Bayesian Filtering and Smoothing (Institute of Mathematical Statistics Textbooks)
Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in...
Adaptive Filter Theory (5th Edition)
Adaptive Filter Theory, 5e, is ideal for courses in Adaptive Filters.
Haykin examines both the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fifth edition, this highly successful book has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible.
