<p><span>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 presen
Introduction to Bayesian Tracking and Particle Filters
β Scribed by Lawrence D. Stone; Roy L. Streit; Stephen L. Anderson
- Publisher
- Springer Nature
- Year
- 2023
- Tongue
- English
- Leaves
- 124
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
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 targetβs 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.
π SIMILAR VOLUMES
<p><b>Presents the Bayesian approach to statistical signal processing for a variety of useful model sets</b><b>Β </b></p> <p>This book aims to give readers a unified Bayesian treatment starting from the basics (Bayeβs rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation
New Bayesian approach helps you solve tough problems in signal processing with ease. Signal processing is based on this fundamental conceptthe extraction of critical information from noisy, uncertain data. Most techniques rely on underlying Gaussian assumptions for a solution, but what happens when
Presents the Bayesian approach to statistical signal processing for a variety of useful model sets <br><br>This book aims to give readers a unified Bayesian treatment starting from the basics (Bayeβs rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techn
The first comprehensive development of Bayesian Bounds for parameter estimation and nonlinear filtering/trackingBayesian estimation plays a central role in many signal processing problems encountered in radar, sonar, communications, seismology, and medical diagnosis. There are often highly nonlinear