Overview
- Provides a quick and insightful introduction to Bayesian Particle Filtering
- Requires only basic knowledge of probability and statistics
- Illustrates and motivates cardinal concepts with practical examples and minimal mathematical complexity
Part of the book series: Studies in Big Data (SBD, volume 126)
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Table of contents (5 chapters)
Keywords
About this book
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. It also introduces the reader to multiple target tracking by presenting useful approximate methods that are easy to implement compared to full-blown multiple target trackers.
The book presents the basic concepts of Bayesian inference and demonstrates the power of the Bayesian method through numerous applications of particle filters to tracking and smoothing problems. It emphasizes target motion models that incorporate knowledge about the target’s behavior in a natural fashion rather than assumptions made for mathematical convenience.
The background provided by this book allows a person to quickly become a productive member of a project team using Bayesian filtering and to develop new methods and techniques for problems the team may face.
Authors and Affiliations
Bibliographic Information
Book Title: Introduction to Bayesian Tracking and Particle Filters
Authors: Lawrence D. Stone, Roy L. Streit, Stephen L. Anderson
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-031-32242-6
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-32241-9Published: 01 June 2023
Softcover ISBN: 978-3-031-32244-0Due: 30 June 2023
eBook ISBN: 978-3-031-32242-6Published: 31 May 2023
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: VI, 118
Number of Illustrations: 5 b/w illustrations, 53 illustrations in colour
Topics: Data Engineering, Bayesian Inference, Big Data