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Introduction to Bayesian Tracking and Particle Filters

Part of the Studies in Big Data Book Series (volume 126)

DOI: 10.1007/978-3-031-32242-6

ISBN: 978-3-031-32241-9

Publisher: Springer International Publishing - Springer Journals

Jun 1, 2023

LitStream

Chapter 1Introduction

Stone, Lawrence D.; Streit, Roy L.; Anderson, Stephen L.

Pages: 1 –3

[This book provides a quick but insightful introduction to Bayesian tracking and particle filtering for a person who has some background in probability and statistics and wishes to learn the basics of single target tracking.]
LitStream

Chapter 2Bayesian Single Target Tracking

Stone, Lawrence D.; Streit, Roy L.; Anderson, Stephen L.

Pages: 5 –44

[This chapter develops the Bayes-Markov recursion for single target tracing. It begins by introducing Bayesian inference and the Bayesian inference recursion. Bayesian inference is a process for estimating parameters. It involves a prior probability distribution on the values of the parameters...
LitStream

Chapter 3Bayesian Particle Filtering

Stone, Lawrence D.; Streit, Roy L.; Anderson, Stephen L.

Pages: 45 –80

[This chapter defines Bayesian particle filtering and applies it to tracking problems that are difficult to solve by Kalman filter methods. In addition, it shows that particle filters can be applied to solve problems other than tracking. The process of computing the posterior distribution on the...
LitStream

Chapter 4Simple Multiple Target Tracking

Stone, Lawrence D.; Streit, Roy L.; Anderson, Stephen L.

Pages: 81 –98

[This chapter develops a basic multiple target tracker which will be useful in relatively simple multiple target tracking situations. The difficulties in multiple target tracking arise when the number of targets is uncertain and there is ambiguity in deciding which target generated which...
LitStream

Chapter 5Intensity Filters

Stone, Lawrence D.; Streit, Roy L.; Anderson, Stephen L.

Pages: 99 –118

[This chapter deals with problems in which the number of targets is a time-varying random process to be estimated. The estimate is in terms of a target intensity function that specifies the expected number of targets per unit state space. When this function is integrated over a subset of the...

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