This text is geared towards a onesemester graduatelevel course instatistical signal processing and estimation theory. The authorbalances technical detail with practical and implementation issues,delivering an exposition that is both theoretically rigorous andapplicationoriented. The book covers topics such as minimum varianceunbiased estimators, the CramerRao bound, best linear unbiasedestimators, maximum likelihood estimation, recursive least squares,Bayesian estimation techniques, and the Wiener and Kalman filters.The author provides numerous examples, which illustrate both theoryand applications for problems such as highresolution spectralanalysis, system identification, digital filter design, adaptivebeamforming and noise cancellation, and tracking and localization.The primary audience will be those involved in the design andimplementation of optimal estimation algorithms on digital computers.The text assumes that you have a background in probability and randomprocesses and linear and matrix algebra and exposure to basic signalprocessing. Students as well as researchers and practicing engineerswill find the text an invaluable introduction and resource for scalarand vector parameter estimation theory and a convenient reference forthe design of successive parameter estimation algorithms.

Authors: Kay S.M.  Pages: 303 Year: 1993 
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