Introduction to Bayesian Tracking and Particle Filters

Introduction to Bayesian Tracking and Particle Filters
Author: Lawrence D. Stone
Publisher: Springer Nature
Total Pages: 124
Release: 2023-05-31
Genre: Computers
ISBN: 3031322428


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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.


Introduction to Bayesian Tracking and Particle Filters
Language: en
Pages: 124
Authors: Lawrence D. Stone
Categories: Computers
Type: BOOK - Published: 2023-05-31 - Publisher: Springer Nature

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This book provides a quick but insightful introduction to Bayesian tracking and particle filtering for a person who has some background in probability and stati
Beyond the Kalman Filter: Particle Filters for Tracking Applications
Language: en
Pages: 328
Authors: Branko Ristic
Categories: Technology & Engineering
Type: BOOK - Published: 2003-12-01 - Publisher: Artech House

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For most tracking applications the Kalman filter is reliable and efficient, but it is limited to a relatively restricted class of linear Gaussian problems. To s
Bayesian Filtering and Smoothing
Language: en
Pages: 255
Authors: Simo Särkkä
Categories: Computers
Type: BOOK - Published: 2013-09-05 - Publisher: Cambridge University Press

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A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
An Introduction to Sequential Monte Carlo
Language: en
Pages: 378
Authors: Nicolas Chopin
Categories: Mathematics
Type: BOOK - Published: 2020-10-01 - Publisher: Springer Nature

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This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the se
Bayesian Estimation and Tracking
Language: en
Pages: 400
Authors: Anton J. Haug
Categories: Mathematics
Type: BOOK - Published: 2012-05-29 - Publisher: John Wiley & Sons

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A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian syste