Bayesian estimation and tracking : a practical guide / Anton J. Haug.

Format:

Book

Author/Creator:

Haug, Anton J., 1941-

Language:

English

Subjects (All):

Bayesian statistical decision theory.

Automatic tracking--Mathematics.

Automatic tracking.

Estimation theory.

Physical Description:

1 online resource (397 p.)

Edition:

1st edition

Place of Publication:

Hoboken, N.J. : Wiley, 2012.

Language Note:

English

System Details:

text file

Summary:

"This book presents a practical approach to estimation methods that are designed to provide a clear path to programming all algorithms. Readers are provided with a firm understanding of Bayesian estimation methods and their interrelatedness. Starting with fundamental principles of Bayesian theory, the book shows how each tracking filter is derived from a slight modification to a previous filter. Such a development gives readers a broader understanding of the hierarchy of Bayesian estimation and tracking. Following the discussions about each tracking filter, the filter is put into block diagram form for ease in future recall and reference. The book presents a completely unified approach to Bayesian estimation and tracking, and this is accomplished by showing that the current posterior density for a state vector can be linked to its previous posterior density through the use of Bayes' Law and the Chapman-Kolmogorov integral. Predictive point estimates are then shown to be density-weighted integrals of nonlinear functions. The book also presents a methodology that makes implementation of the estimation methods simple (or, rather, simpler than they have been in the past). Each algorithm is accompanied by a block diagram that illustrates how all parts of the tracking filter are linked in a never-ending chain, from initialization to the loss of track. These filter block diagrams provide a ready picture for implementing the algorithms into programmable code. In addition, four completely worked out case studies give readers examples of implementation, from simulation models that generate noisy observations to worked-out applications for all tracking algorithms. This book also presents the development and application of track performance metrics, including how to generate error ellipses when implementing in real-world applications, how to calculate RMS errors in simulation environments, and how to calculate Cramer-Rao lower bounds for the RMS errors. These are also illustrated in the case study presentations"-- Provided by publisher.

Contents:

pt. 1. Preliminaries

pt. 2. The Gaussian assumption : a family of Kalman filter estimators

pt. 3. Monte Carlo methods

pt. 4. Additional case studies.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

9786613664174

9781280687235

1280687231

9781118287804

1118287800

9781118287835

1118287835

9781118287798

1118287797

OCLC:

798710560