Digital Spectral Analysis: Parametric, Non-Parametric and Advanced Methods

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.

Keywords: Signal Processing, Guide to spectral analysis domain, fundamental of spectral analysis, spectral analysis and digital signal processing, estimation in spectral analysis, spectral analysis and time-series models, non-parametric methods and spectral analysis, spectral analysis by stationary time series modeling, spectral analysis and minimum variance, spectral analysis and subspace-based estimators, spectral analysis of non-stationary random signals, spectral analysis and space time adaptive processes, spectral analysis and particle filtering, and spectral analysis and tracking of varying sinusoids