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A comprehensive introduction to the mathematical principles and algorithms in statistical signal processing and modern neural networks.
This text is an expanded version of a graduate course on advanced signal processing at the Johns Hopkins University Whiting school program for professionals with students from electrical engineering, physics, computer and data science, and mathematics backgrounds. It covers the theory underlying applications in statistical signal processing including spectral estimation, linear prediction, adaptive filters, and optimal processing of uniform spatial arrays. Unique among books on the subject, it also includes a comprehensive introduction to modern neural networks with examples in time series prediction and image classification.
Coverage includes:
- Mathematical structures of signal spaces and matrix factorizations
- linear time-invariant systems and transforms
- Least squares filters
- Random variables, estimation theory, and random processes
- Spectral estimation and autoregressive signal models
- linear prediction and adaptive filters
- Optimal processing of linear arrays
- Neural networks
