Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory

This comprehensive guide is a must-read for anyone looking to master the art of parameter estimation in signal processing.

With a focus on practical applications and implementation issues, this book provides an exposition that is both theoretically rigorous and application-oriented.

Covering topics such as minimum variance unbiased estimators, Cramer-Rao bound, best linear unbiased estimators, maximum likelihood estimation, recursive least squares, Bayesian estimation techniques, and the Wiener and Kalman filters, this book is an invaluable introduction and resource for students, researchers, and practicing engineers.

Whether you're looking to design and implement optimal estimation algorithms on digital computers or simply want to gain a deeper understanding of signal processing, this book is the perfect tool for you.

So why wait? Get your copy today and start unlocking the secrets of parameter estimation in signal processing!