In this edition the book has been brought up to date by increasing the emphasis on the above areas and including several new developments. The major addition are: a unified presention of the fasst recursive least-squares alogorithms for adaptive processing;the discusison of several eigenvector methold of spectrum estimation such as MUSIC, minimum-norm, esprit, spatial smoothing for coherent signals,and others; and discussion of the Schur algorithm for linear prediction admitting efficient parallel implementations, and the more efficient split and split Levinson algorithms. Moreover, older but basic material has been added such as an expanded discussion of Kalman filtering and discussion of classical statistical estimation concepts such maximum likelihood, Cramer-Rao Bound, and asymptotic statistical properties applied to linear prediction and eigenstructure methods.

1 Random Signals
2 Some Signal Processing Applications
3 Spectral Factorization
4 Linear Estimation of Signals
5 Linear Prediction
6 Spetcrum Estimation
7 Adaptive Filters