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Informationen zum Autor ANTON J. HAUG, PhD, is member of the technical staff at the Applied Physics Laboratory at The Johns Hopkins University, where he develops advanced target tracking methods in support of the Air and Missile Defense Department. Throughout his career, Dr. Haug has worked across diverse areas such as target tracking; signal and array processing and processor design; active and passive radar and sonar design; digital communications and coding theory; and time- frequency analysis. Klappentext A practical approach to estimating and tracking dynamic systems in real-worl applicationsMuch 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(r) 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. Zusammenfassung 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. Inhaltsverzeichnis Preface xv Acknowledgments xvii List of Figures Xix List of Tables xxv PART I PRELIMINARIES 1 Introduction 3 1.1 Bayesian Inference 4 1.2 Bayesian Hierarchy of Estimation Methods 5 1.3 Scope of This Text 6 1.3.1 Objective 6 1.3.2 Chapter Overview and Prerequisites 6 1.4 Modeling and Simulation with MATLAB® 8 References 9 2 Preliminary Mathematical Concepts 11 2.1 A Very Brief Overview of Matrix Linear Algebra 11 2.1.1 Vector and Matrix Conventions and Notation 11 2.1.2 Sums and Products 12 2.1.3 Matrix Inversion 13 2.1.4 Block Matrix Inversion 14 2.1.5 Matrix Square Root 15 2.2 Vector Point Generators 16 2.3 Approximating Nonlinear Multidimensional Functions with Multidimensional Arguments 19 2.3.1 Approximating Scalar Nonlinear Functions 19 2.3.2 Approximating Multidimensional Nonlinear Functions 23 2.4 Overview of Multivariate Statistics 29 2.4.1 General Definitions 29 2.4.2 The Gaussian Density 32 References 40 3 General Concepts of Bayesian Estimation 42 3.1 Bayesian Estimation 43 3.2 Point Estimators 43 3.3 Introduction to Recursive Bayesian Filtering of Probability Density Functions 46 3.4 Introduction to Recursive Bayesian Estimation of the State Mean and Covariance 49 3.4.1 State Vector Prediction 50 3.4.2 State Vector Update 5...
