Castanie F. Digital Spectral Analysis...2011

Textbook in PDF format

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.
Fundamentals
Digital Signal Processing
Introduction to Estimation Theory with Application in Spectral Analysis
Time-Series Models
Non-Parametric Methods
Spectral Analysis by Parametric Modeling
Minimum Variance
Subspace-Based Estimators and Application to Partially Known Signal Subspaces
Chapter 9 Multidimensional Harmonic Retrieval: Exact, Asymptotic, and Modified Crame?ReRao Bounds (pages 251–286):
Chapter 10 Introduction to Spectral Analysis of Non?Stationary Random Signals (pages 287–299):
Chapter 11 Spectral Analysis of Non?uniformly Sampled Signals (pages 301–316):
Chapter 12 Space–Time Adaptive Processing (pages 317–360):
Chapter 13 Particle Filtering and Tracking of Varying Sinusoids