Bayesian Estimation and Tracking: A Practical Guide

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John Wiley & Sons, May 29, 2012 - Mathematics - 400 pages
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A practical approach to estimating and tracking dynamicsystems in real-worl applications

Much of the literature on performing estimation for non-Gaussiansystems is short on practical methodology, while Gaussian methodsoften lack a cohesive derivation. Bayesian Estimation andTracking addresses the gap in the field on both accounts,providing readers with a comprehensive overview of methods forestimating both linear and nonlinear dynamic systems driven byGaussian and non-Gaussian noices.

Featuring a unified approach to Bayesian estimation andtracking, the book emphasizes the derivation of all trackingalgorithms within a Bayesian framework and describes effectivenumerical methods for evaluating density-weighted integrals,including linear and nonlinear Kalman filters for Gaussian-weightedintegrals and particle filters for non-Gaussian cases. The authorfirst emphasizes detailed derivations from first principles ofeeach estimation method and goes on to use illustrative anddetailed step-by-step instructions for each method that makescoding of the tracking filter simple and easy to understand.

Case studies are employed to showcase applications of thediscussed topics. In addition, the book supplies block diagrams foreach algorithm, allowing readers to develop their own MATLAB®toolbox of estimation methods.

Bayesian Estimation and Tracking is an excellent book forcourses on estimation and tracking methods at the graduate level.The book also serves as a valuable reference for researchscientists, mathematicians, and engineers seeking a deeperunderstanding of the topics.

 

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The discussion is quite organized and well presented. It helps understand the development of different types of Bayesian filtering and show how these filtering techniques are connected.

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rohit

Contents

List of Tables
Preliminary Mathematical Concepts
General Concepts of Bayesian Estimation
Case Studies Preliminary Discussions
The Gaussian Noise Case Multidimensional Integration
The Linear Class of Kalman Filters
References
The Sigma Point Class The Finite Difference Kalman Filter
The Monte Carlo Kalman Filter
Summary of Gaussian Kalman Filters
Performance Measures for the Family of Kalman Filters
Introduction to Monte Carlo Methods
Sequential Importance Sampling Particle Filters
The Generalized Monte Carlo Particle Filter
A Spherical Constant Velocity Model for Target Tracking
Tracking a Falling Rigid Body Using Photogrammetry

The Sigma Point Class The Unscented Kalman Filter
The Sigma Point Class The Spherical Simplex Kalman Filter
The Sigma Point Class The GaussHermite Kalman Filter
Sensor Fusion using Photogrammetric and Inertial
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About the author (2012)

ANTON J. HAUG, PhD, is member of the technical staff atthe Applied Physics Laboratory at The Johns Hopkins University,where he develops advanced target tracking methods in support ofthe Air and Missile Defense Department. Throughout his career, Dr.Haug has worked across diverse areas such as target tracking;signal and array processing and processor design; active andpassive radar and sonar design; digital communications and codingtheory; and time- frequency analysis.

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