Gift Guide

Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods [NOOK Book]


New Bayesian approach helps you solve tough problems in signal processing with ease

Signal processing is based on this fundamental concept—the extraction of critical information from noisy, uncertain data. Most techniques rely on underlying Gaussian assumptions for a solution, but what happens when these assumptions are erroneous? Bayesian techniques circumvent this limitation by offering a completely different approach that can easily ...

See more details below
Bayesian Signal Processing: Classical, Modern and Particle Filtering Methods

Available on NOOK devices and apps  
  • NOOK Devices
  • Samsung Galaxy Tab 4 NOOK 7.0
  • Samsung Galaxy Tab 4 NOOK 10.1
  • NOOK HD Tablet
  • NOOK HD+ Tablet
  • NOOK eReaders
  • NOOK Color
  • NOOK Tablet
  • Tablet/Phone
  • NOOK for Windows 8 Tablet
  • NOOK for iOS
  • NOOK for Android
  • NOOK Kids for iPad
  • PC/Mac
  • NOOK for Windows 8
  • NOOK for PC
  • NOOK for Mac
  • NOOK for Web

Want a NOOK? Explore Now

NOOK Book (eBook)
$93.49 price
(Save 42%)$164.00 List Price
Note: This NOOK Book can be purchased in bulk. Please email us for more information.


New Bayesian approach helps you solve tough problems in signal processing with ease

Signal processing is based on this fundamental concept—the extraction of critical information from noisy, uncertain data. Most techniques rely on underlying Gaussian assumptions for a solution, but what happens when these assumptions are erroneous? Bayesian techniques circumvent this limitation by offering a completely different approach that can easily incorporate non-Gaussian and nonlinear processes along with all of the usual methods currently available.

This text enables readers to fully exploit the many advantages of the "Bayesian approach" to model-based signal processing. It clearly demonstrates the features of this powerful approach compared to the pure statistical methods found in other texts. Readers will discover how easily and effectively the Bayesian approach, coupled with the hierarchy of physics-based models developed throughout, can be applied to signal processing problems that previously seemed unsolvable.

Bayesian Signal Processing features the latest generation of processors (particle filters) that have been enabled by the advent of high-speed/high-throughput computers. The Bayesian approach is uniformly developed in this book's algorithms, examples, applications, and case studies. Throughout this book, the emphasis is on nonlinear/non-Gaussian problems; however, some classical techniques (e.g. Kalman filters, unscented Kalman filters, Gaussian sums, grid-based filters, et al) are included to enable readers familiar with those methods to draw parallels between the two approaches.

Special features include:

  • Unified Bayesian treatment starting from the basics (Bayes's rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation techniques (sequential Monte Carlo sampling)
  • Incorporates "classical" Kalman filtering for linear, linearized, and nonlinear systems; "modern" unscented Kalman filters; and the "next-generation" Bayesian particle filters

  • Examples illustrate how theory can be applied directly to a variety of processing problems

  • Case studies demonstrate how the Bayesian approach solves real-world problems in practice

  • MATLAB notes at the end of each chapter help readers solve complex problems using readily available software commands and point out software packages available

  • Problem sets test readers' knowledge and help them put their new skills into practice

The basic Bayesian approach is emphasized throughout this text in order to enable the processor to rethink the approach to formulating and solving signal processing problems from the Bayesian perspective. This text brings readers from the classical methods of model-based signal processing to the next generation of processors that will clearly dominate the future of signal processing for years to come. With its many illustrations demonstrating the applicability of the Bayesian approach to real-world problems in signal processing, this text is essential for all students, scientists, and engineers who investigate and apply signal processing to their everyday problems.

Read More Show Less

Product Details

Meet the Author

JAMES V. CANDY, PhD, is Chief Scientist for Engineering, founder, and former director of the Center for Advanced Signal & Image Sciences at the Lawrence Livermore National Laboratory. Dr. Candy is also an Adjunct Full Professor at the University of California, Santa Barbara, a Fellow of the IEEE, and a Fellow of the Acoustical Society of America. Dr. Candy has published more than 225 journal articles, book chapters, and technical reports. He is also the author of Signal Processing: Model-Based Approach, Signal Processing: A Modern Approach, and Model-Based Signal Processing (Wiley). Dr. Candy was awarded the IEEE Distinguished Technical Achievement Award for his development of model-based signal processing and the Acoustical Society of America Helmholtz-Rayleigh Interdisciplinary Silver Medal for his contributions to acoustical signal processing and underwater acoustics.
Read More Show Less

Table of Contents




1. Introduction.

1.1 Introduction.

1.2 Bayesian Signal Processing.

1.3 Simulation-Based Approach to Bayesian Processing.

1.4 Bayesian Model-Based Signal Processing.

1.5 Notation and Terminology.



2. Bayesian Estimation.

2.1 Introduction.

2.2 Batch Bayesian Estimation.

2.3 Batch Maximum Likelihood Estimation.

2.4 Batch Minimum Variance Estimation.

2.5 Sequential Bayesian Estimation.

2.6 Summary.



3. Simulation-Based Bayesian Methods.

3.1 Introduction.

3.2 Probability Density Function Estimation.

3.3 Sampling Theory.

3.4 Monte Carlo Approach.

3.5 Importance Sampling.

3.6 Sequential Importance Sampling.

3.7 Summary.



4. State-Space Models for Bayesian Processing.

4.1 Introduction.

4.2 Continuous-Time State-Space Models.

4.3 Sampled-Data State-Space Models.

4.4 Discrete-Time State-Space Models.

4.5 Gauss-Markov State-Space Models.

4.6 Innovations Model.

4.7 State-Space Model Structures.

4.8 Nonlinear (Approximate) Gauss-Markov State-Space Models.

4.9 Summary.



5. Classical Bayesian State-Space Processors.

5.1 Introduction.

5.2 Bayesian Approach to the State-Space.

5.3 Linear Bayesian Processor (Linear Kalman Filter).

5.4 Linearized Bayesian Processor (Linearized Kalman Filter).

5.5 Extended Bayesian Processor (Extended Kalman Filter).

5.6 Iterated-Extended Bayesian Processor (Iterated-Extended Kalman Filter).

5.7 Practical Aspects of Classical Bayesian Processors.

5.8 Case Study: RLC Circuit Problem.

5.9 Summary.



6. Modern Bayesian State-Space Processors.

6.1 Introduction.

6.2 Sigma-Point (Unscented) Transformations.

6.3 Sigma-Point Bayesian Processor (Unscented Kalman Filter).

6.4 Quadrature Bayesian Processors.

6.5 Gaussian Sum (Mixture) Bayesian Processors.

6.6 Case Study: 2D-Tracking Problem.

6.7 Summary.



7. Particle-Based Bayesian State-Space Processors.

7.1 Introduction.

7.2 Bayesian State-Space Particle Filters.

7.3 Importance Proposal Distributions.

7.4 Resampling.

7.5 State-Space Particle Filtering Techniques.

7.6 Practical Aspects of Particle Filter Design.

7.7 Case Study: Population Growth Problem.

7.8 Summary.



8. Joint Bayesian State/Parametric Processors.

8.1 Introduction.

8.2 Bayesian Approach to Joint State/Parameter Estimation.

8.3 Classical/Modern Joint Bayesian State/Parametric Processors.

8.3.1 Classical Joint Bayesian Processor.

8.3.2 Modern Joint Bayesian Processor.

8.4 Particle-Based Joint Bayesian State/Parametric Processors.

8.5 Case Study: Random Target Tracking using a Synthetic Aperture Towed Array.

8.6 Summary.



9. Discrete Hidden Markov Model Bayesian Processors.

9.1 Introduction.

9.2 Hidden Markov Models.

9.3 Properties of the Hidden Markov Model.

9.4 HMM Observation Probability: Evaluation Problem.

9.5 State Estimation in HMM: The Viterbi Technique.

9.6 Parameter Estimation in HMM: The EM/Baum-Welch Technique.

9.7 Case Study: Time-Reversal Decoding.

9.8 Summary.



10. Bayesian Processors for Physics-Based Applications.

10.1 Optimal Position Estimation for the Automatic Alignment.

10.2 Broadband Ocean Acoustic Processing.

10.3 Bayesian Processing for Biothreats.

10.4 Bayesian Processing for the Detection of Radioactive Sources.


Appendix A. Probability & Statistics Overview.

A.1 Probability Theory.

A.2 Gaussian Random Vectors.

A.3 Uncorrelated Transformation: Gaussian Random Vectors.


Read More Show Less

Customer Reviews

Average Rating 5
( 1 )
Rating Distribution

5 Star


4 Star


3 Star


2 Star


1 Star


Your Rating:

Your Name: Create a Pen Name or

Barnes & Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation


  • - By submitting a review, you grant to Barnes & and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Terms of Use.
  • - Barnes & reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously
Sort by: Showing 1 Customer Reviews
  • Anonymous

    Posted January 21, 2014


    Ya... i relise that. I need to talk to him.

    Was this review helpful? Yes  No   Report this review
Sort by: Showing 1 Customer Reviews

If you find inappropriate content, please report it to Barnes & Noble
Why is this product inappropriate?
Comments (optional)