In order to maximize useful signal quality in low signal-to-noise ratio processing, the use of a priori information or models for both the expected and the random signals is recommended. This work addresses model-based signal processing (MBSP) theory at three levels: the applied mathematical background of signal processing, MBSP algorithms and techniques, and complex case studies and applications in the field.

The introduction, including chapters 1 to 4, presents some basic tools for the analysis, prediction, and modeling of signals, including spectral signal representation, stochastic processes theory, space-time wave analysis, state-space signal models, and estimation theory. The core of the work, spanning the next four chapters, focuses on MBSP methodology, discussing several parametric processing methods, some linear and nonlinear state-space based algorithms, and both parametric and state-space approaches for adaptive processing techniques. The final chapter addresses a large spectrum of MBSP applications in some quite varied domains, including space vehicle radar tracking, quality inspection and failure detection for technical structures, and space-time acoustic signal processing using sonar array antennas and dispersed wave propagation studies.

Each chapter of the book includes MATLAB-based examples and graphical representations to elucidate the text, extended references and MATLAB functions, and sets of problems related to the subjects discussed. One of the three appendices is devoted to a brief presentation of an interactive MBSP software package, working in the MATLAB environment. For graduate-level readers, the existence of another appendix offering solutions for some of the most complex end-of-chapter problems would be welcomed.

Given its extensive, but very cohesive and accessible coverage of the model-based approach to the signal processing domain, and, in particular, the great number of included applications and case studies, this book could be very well appreciated by both students and specialists in the field.