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Informationen zum Autor Chaw-Bing Chang is a Senior Staff Member of the BMDS (Ballistic Missile Defense System) Integration Group at MIT Lincoln Laboratory. Chang has forty years of experience in applying estimation and association techniques. Keh-Ping Dunn is a Senior Staff Member of the BMDS Integration Group at MIT Lincoln Laboratory. Dunn has forty years of experience in applying estimation and association techniques. Klappentext A rigorous introduction to the theory and applications of state estimation and association, an important area in aerospace, electronics, and defense industries. Zusammenfassung A rigorous introduction to the theory and applications of state estimation and association, an important area in aerospace, electronics, and defense industries. Applied state estimation and association is an important area for practicing engineers in aerospace, electronics, and defense industries, used in such tasks as signal processing, tracking, and navigation. This book offers a rigorous introduction to both theory and application of state estimation and association. It takes a unified approach to problem formulation and solution development that helps students and junior engineers build a sound theoretical foundation for their work and develop skills and tools for practical applications. Chapters 1 through 6 focus on solving the problem of estimation with a single sensor observing a single object, and cover such topics as parameter estimation, state estimation for linear and nonlinear systems, and multiple model estimation algorithms. Chapters 7 through 10 expand the discussion to consider multiple sensors and multiple objects. The book can be used in a first-year graduate course in control or system engineering or as a reference for professionals. Each chapter ends with problems that will help readers to develop derivation skills that can be applied to new problems and to build computer models that offer a useful set of tools for problem solving. Readers must be familiar with state-variable representation of systems and basic probability theory including random and stochastic processes. Inhaltsverzeichnis Preface xvii About the Authors xix Acknowledgments xxi Introduction xxiii 1 Parameter Estimation 1 2 State Estimation for Linear Systems 51 3 State Estimation for Nonlinear Systems 99 4 Practical Considerations in Kalman Filter Design 141 5 Multiple Model Estimation Algorithms 197 6 Sampling Techniques for State Estimations 227 7 State Estimation with Multiple Sensor Systems 271 8 Estimation and Association with Uncertain Measurement Origin 303 9 Multiple Hypothesis Tracking Algorithm 257 10 Multiple Sensor Correlation and Fusion with Biased Measurements 391 Concluding Remarks 413 Appendix A Matrix Inversion Lemma 417 Appendix B Notation and Variables 419 Appendix C Definition of Terminology Used in Tracking 425 Index 431...
