Bayesian multiple target tracking /

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This book views multiple target tracking as a Bayesian inference problem. Within this framework it develops the theory of single target tracking, multiple target tracking, and likelihood ratio detection and tracking. In addition to providing a detailed description of a basic particle filter that imp...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Stone, Lawrence D., 1942- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Boston [Massachusetts] ; London [England] : Artech House, 2014.
Edición:Second edition.
Colección:Artech House radar library.
Temas:
Tracking radar > Mathematics.
Bayesian statistical decision theory.
Radar de poursuite > Mathématiques.
Théorie de la décision bayésienne.
TECHNOLOGY & ENGINEERING > Mechanical.
Bayesian statistical decision theory
Tracking radar > Mathematics
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Bayesian Multiple Target Tracking Second Edition; Contents; Preface; Introduction; Acknowledgments; Chapter 1 Tracking Problems; 1.1 DESCRIPTION OF TRACKING PROBLEM; 1.2 EXAMPLE 1: TRACKING A SURFACE SHIP; 1.3 EXAMPLE 2: BEARINGS-ONLY TRACKING; 1.4 EXAMPLE 3: PERISCOPE DETECTION AND TRACKING; 1.5 EXAMPLE 4: TRACKING MULTIPLE TARGETS; 1.6 SUMMARY; Chapter 2 Bayesian Inference and Likelihood Functions; 2.1 THE CASE FOR BAYESIAN INFERENCE; 2.2 THE LIKELIHOOD FUNCTION AND BAYES' THEOREM; 2.3 EXAMPLES OF LIKELIHOOD FUNCTIONS; Chapter 3 Single Target Tracking; 3.1 BAYESIAN FILTERING.
  • 3.2 KALMAN FILTERING3.3 PARTICLE FILTER IMPLEMENTATION OF NONLINEARFILTERING; 3.4 SUMMARY; Chapter 4 Classical Multiple Target Tracking; 4.1 MULTIPLE TARGET TRACKING; 4.2 MULTIPLE HYPOTHESIS TRACKING; 4.3 INDEPENDENT MULTIPLE HYPOTHESIS TRACKING; 4.4 LINEAR-GAUSSIAN MULTIPLE HYPOTHESIS TRACKING; 4.5 NONLINEAR JOINT PROBABILISTIC DATA ASSOCIATION; 4.6 PROBABILISTIC MULTIPLE HYPOTHESIS TRACKING; 4.7 SUMMARY; 4.8 NOTES; Chapter 5 Multitarget Intensity Filters; 5.1 POINT PROCESS MODEL OF MULTITARGET STATE; 5.2 iFILTER; 5.3 PHD FILTER; 5.4 PGF APPROACH TO THE iFILTER; 5.5 EXTENDED TARGET FILTERS.
  • 5.6 SUMMARY5.7 NOTES; Chapter 6 Multiple Target Tracking Using Tracker-Generated Measurements; 6.1 MAXIMUM A POSTERIORI PENALTY FUNCTION TRACKING; 6.2 PARTICLE FILTER IMPLEMENTATION; 6.3 LINEAR-GAUSSIAN IMPLEMENTATION; 6.4 EXAMPLES; 6.5 SUMMARY; 6.6 NOTES; 6.7 SENSOR ARRAY OBSERVATION MODEL AND SIGNALPROCESSING; Chapter 7 Likelihood Ratio Detection and Tracking; 7.1 BASIC DEFINITIONS AND RELATIONS; 7.2 LIKELIHOOD RATIO RECURSIONS; 7.3 DECLARING A TARGET PRESENT; 7.4 LOW-SNR EXAMPLES OF LRDT; 7.5 THRESHOLDED DATA WITH HIGH CLUTTER RATE; 7.6 GRID-BASED IMPLEMENTATION.
  • 7.7 MULTIPLE TARGET TRACKING USING LRDT7.8 iLRT; 7.9 SUMMARY; 7.10 NOTES; Appendix: Gaussian Density Lemma; About the Authors; Index.

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