Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods (Adaptive and Cognitive Dynamic Systems: S
Presents the Bayesian approach to statistical signal processing for a variety of useful model sets
This book aims to give readers a unified Bayesian treatment starting from the basics (Baye’s rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on “Sequential Bayesian Detection,” a new section on “Ensemble Kalman Filters” as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to “fill-in-the gaps” of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical “sanity testing” lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed and applied. These expansions of the book have been updated to provide a more cohesive discussion of Bayesian processing with examples and applications enabling the comprehension of alternative approaches to solving estimation/detection problems.
The second edition of Bayesian Signal Processing features:
- “Classical” Kalman filtering for linear, linearized, and nonlinear systems; “modern” unscented and ensemble Kalman filters: and the “next-generation” Bayesian particle filters
- Sequential Bayesian detection techniques incorporating model-based schemes for a variety of real-world problems
- Practical Bayesian processor designs including comprehensive methods of performance analysis ranging from simple sanity testing and ensemble techniques to sophisticated information metrics
- New case studies on adaptive particle filtering and sequential Bayesian detection are covered detailing more Bayesian approaches to applied problem solving
- MATLAB® notes at the end of each chapter help readers solve complex problems using readily available software commands and point out other software packages available
- Problem sets included to test readers’ knowledge and help them put their new skills into practice Bayesian
Why Read This Book
You will learn a unified Bayesian framework for tackling real-world signal processing problems, from basic Bayes' rule to advanced sequential Monte Carlo methods. The book pairs rigorous theory with practical particle-filter designs, sequential detectors, and expanded case studies so you can apply Bayesian methods to audio, radar, communications, and adaptive-filtering problems.
Who Will Benefit
Graduate students, DSP engineers, and research scientists who want to apply Bayesian inference, particle filtering, and sequential detection to audio/speech, radar, and communications systems.
Level: Advanced — Prerequisites: Undergraduate-level calculus, probability & statistics, and linear algebra; fundamental DSP concepts (z-transform, FFT, digital filters); familiarity with MATLAB or Python for simulations.
Key Takeaways
- Apply Bayesian inference to formulate and solve estimation and detection problems in signal processing.
- Design and implement particle filters and sequential Monte Carlo methods for nonlinear, non-Gaussian systems.
- Derive and use Kalman, extended Kalman, and ensemble Kalman filters within a Bayesian framework.
- Develop sequential Bayesian detectors and adaptive particle-filter schemes for real-time applications.
- Integrate Bayesian methods into spectral analysis, FFT-based processing, wavelet/time–frequency approaches, and communications/radar case studies.
Topics Covered
- 1. Introduction to Bayesian Signal Processing and Bayes' Rule
- 2. Probabilistic Models for Signals and Noise
- 3. Linear Gaussian Models and the Kalman Filter
- 4. Nonlinear Filtering: Extended and Unscented Kalman Approaches
- 5. Monte Carlo Methods and Importance Sampling
- 6. Sequential Monte Carlo (Particle Filtering) Fundamentals
- 7. Advanced Particle Filtering: Resampling, Adaptation, and Smoothing
- 8. Ensemble Kalman Filters and Hybrid Methods
- 9. Sequential Bayesian Detection and Hypothesis Testing
- 10. Parameter Estimation and Adaptive Filtering Techniques
- 11. Bayesian Approaches to Spectral Analysis, FFTs, and Wavelets
- 12. Case Studies: Audio/Speech, Radar Tracking, and Communications
- 13. Implementation Notes, Practical Considerations, and Examples
- Appendices: Mathematical Background and Algorithmic Recipes
Languages, Platforms & Tools
How It Compares
Complementary to Simo Särkkä's Bayesian Filtering and Smoothing (which emphasizes Gaussian filters) and to Doucet et al.'s Sequential Monte Carlo Methods in Practice (which deeply covers SMC theory); Candy's book bridges both with signal-processing applications and detector design.
