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Bayesian signal processing : classical, modern and particle filtering methods
Candy J., Wiley-Interscience, New York, NY, 2009. 445 pp. Type: Book (9780470180945)
Date Reviewed: Oct 20 2009

This is one of the best texts on advanced signal processing. This is not surprising, considering Candy’s previous textbook [1].

The book deals with one of the most interesting problems in signal processing: extracting useful information from noisy and uncertain data. Candy includes several exercises at the end of each chapter. He provides readers with well-researched information on relevant MATLAB resources, in contrast to other texts that fill several pages with MATLAB code, at the expense of presentation clarity.

In the first chapter, the author introduces Bayesian methodology and presents the essential reasons why it is so useful. Chapter 2 presents the core Bayesian processing procedures--batch processing and sequential processing schemes--and maximum likelihood and minimum variance processing. Chapter 3 details the fundamental principle of sampling, such as Metropolis and Gibbs sampling. It also presents the concept of importance sampling. Chapter 4 develops the state-space approach to signal modeling, which has several well-known practical applications, including speech analysis, acoustics, and process control. Chapter 5 studies the so-called classical Bayesian processors; it shows that under the assumptions of linearity and Gaussianity, the optimal Bayesian processor development leads to the well-known Kalman filter. Several other equivalences with variants of the Kalman filter are thoroughly explored. Chapter 6 focuses on statistical linearization methods and how they are used to derive the unscented Kalman filter. Chapter 7 clearly presents the processing methods of particle filtering, and also discusses some very useful techniques such as bootstrapping. Chapter 8 is entirely devoted to the joint Bayesian state-space particle filter. Chapter 9, which presents hidden Markov models, is more concise and lacks a certain level of clarity. The last chapter--a virtual laboratory--presents how Bayesian techniques are used to solve practical problems, such as laser alignment and radiation detection. The one appendix presents a snapshot of both basic and advanced probabilistic concepts.

As good as it is, the book is not self-contained. The reader should be conversant with the ideas and methods of processing random signals, as a prerequisite. That being said, I highly recommend this text for advanced studies in signal processing. It will also serve as a fine reference for Bayesian techniques.

Reviewer:  Vladimir Botchev Review #: CR137379 (1010-1000)
1) Candy, J.V. Signal processing: the model-based approach. McGraw-Hill, New York, NY, 1986.
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