Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory

Description This book is written onparameter estimation range from highly theoretical to morepractical applied statistics. This book strikes a balance betweenthese 2 extremes. Audience is community involved in design andimplementation of signal processing algorithms. The primary focusis on obtaining optimal estimation algorithms that may beimplemented in a computer. Numerous, worked-out examples. The bookis rich in examples that illustrate the theory (of statisticalsignal processing) and examples that apply the theory to actualsignal processing problems of current interest. For Sale in Indiansubcontinent only 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. 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.