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Statistical signal processing is the branch of signal processing that treats signals as random processes and uses probability theory and statistical inference to model, estimate, detect, and extract information in the presence of noise and uncertainty. Books in this category typically cover stochastic process models, estimation theory (ML, MAP, Cramér–Rao), detection and hypothesis testing, spectral analysis, linear and nonlinear filtering (Wiener, Kalman, particle filters), adaptive and array processing, and modern Bayesian and Monte Carlo methods, often with algorithmic and computational examples in MATLAB/Python. Applications span communications, radar/sonar, audio, biomedical signals, and remote sensing, and the material is aimed at advanced undergraduates, graduate students, and practicing engineers and researchers needing rigorous probabilistic tools for signal analysis.

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