Digital Spectral Analysis

Digital Spectral Analysis provides a single source that
offers complete coverage of the spectral analysis domain. This
self-contained work includes details on advanced topics that are
usually presented in scattered sources throughout the
literature.

The theoretical principles necessary for the understanding of
spectral analysis are discussed in the first four chapters:
fundamentals, digital signal processing, estimation in spectral
analysis, and time-series models.

An entire chapter is devoted to the non-parametric methods most
widely used in industry.

High resolution methods are detailed in a further four chapters:
spectral analysis by stationary time series modeling, minimum
variance, and subspace-based estimators.

Finally, advanced concepts are the core of the last four chapters:
spectral analysis of non-stationary random signals, space time
adaptive processing: irregularly sampled data processing, particle
filtering and tracking of varying sinusoids.

Suitable for students, engineers working in industry, and academics
at any level, this book provides a rare complete overview of the
spectral analysis domain.