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Statistical and Adaptive Signal Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array Processing

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This authoritative volume on statistical and adaptive signal processing offers you a unified, comprehensive and practical treatment of spectral estimation, signal modeling, adaptive filtering, and array processing. Packed with over 3,000 equations and more than 300 illustrations, this unique resource provides you with balanced coverage of implementation issues, applications, and theory, making it a smart choice for professional engineers and students alike.

From the fundamentals of discrete-time signal processing and linear signal models, to optimum linear filters and least-squares filtering and prediction, you get in-depth information on a broad range of critical topics from leading experts in industry and academia. This invaluable reference provides clear examples, problem sets, and computer experiments that help you master the material and learn how to implement various methods presented in the book. You also find a set of Matlab® functions that illustrate the use of various techniques and can be used to solve real-world problems in the field.

This authoritative volume on statistical and adaptive signal processing offers you a unified, comprehensive and practical treatment of spectral estimation, signal modeling, adaptive filtering, and array processing. Packed with over 3,000 equations and more than 300 illustrations, this unique resource provides you with balanced coverage of implementation issues, applications, and theory, making it a smart choice for professional engineers and students alike.

About the Author

Dimitris G. Manolakis is a member of the technical staff at M.I.T. Lincoln Laboratory. Previously, he was a principal member of the research staff at Riverside Research Institute. Dr. Manolakis has taught at the University of Athens, Northeastern University, Boston College, and Worchester Polytechnic Institute. He received his Ph.D. in electrical engineering from the University of Athens, Greece.

Vinay K. Ingle is an associate professor of electrical and computer engineering at Northeastern University. He has broad research experience and has taught courses on signal and image processing, stochastic processes, and estimation theory. Dr. Ingle received his Ph.D. in electrical engineering from Rensselaer Polytechnic Institute.

Stephen M. Kogon is a member of the technical staff at M.I.T. Lincoln Laboratory. Previously, he has been associated with Raytheon Co., Boston College, and Georgia Tech Research Institute. Dr. Kogon received his Ph.D. in electrical engineering from the Georgia Institute of Technology.

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