Description
Pearson India Statistical Signal Processing Volume 1 by Steven M Kay
This book is written on parameter estimation range from highly theoretical to more practical applied statistics. This book strikes a balance between these 2 extremes. Audience is community involved in design and implementation of signal processing algorithms. The primary focus is on obtaining optimal estimation algorithms that may be implemented in a computer. Numerous, worked-out examples. The book is rich in examples that illustrate the theory (of statistical signal processing) and examples that apply the theory to actual signal processing problems of current interest. Features Describes the field of parameter estimation based on time series data. Provides a summary of principal approaches as well as a “roadmap” to use in the selection of an estimator. Extends many of the results for real data/real parameters to complex data/complex parameters. Summarizes as examples many of the important estimators used in practice. Table Of Contents Introduction. Minimum Variance Unbiased Estimation. Cramer-Rao Lower Bound. Linear Models. General Minimum Variance Unbiased Estimation. Best Linear Unbiased Estimators. Maximum Likelihood Estimation. Least Squares. Method of Moments. The Bayesian Philosophy. General Bayesian Estimators. Linear Bayesian Estimators. Kalman Filters. Summary of Estimators.