A broad discussion is presented of spectral estimation techniques and their implementation. The topics addressed include: reviews of linear systems, transform theory, matrix algebra, and random process theory; classical spectral estimation; parametric models of random processes; autoregressive process and spectrum properties; block data algorithms and sequential data algorithms in autoregressive spectral estimation. Also discussed are: autoregressive-moving average spectral estimation; Prony's method; minimum variance spectral estimation; eigenanalysis-based frequency estimation; summary of spectral estimators; multichannel spectral estimation; and two-dimensional spectral estimation.
Publication:
Englewood Cliffs