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Algorithms For Statistical Signal Processing by Proakis, John G., Rader, Charles M., Ling, Fuyun, Moonen, Marc, Proudler, Ian K., Nikias, Chrysostomos L., John G. Proakis, Charles M. Rader, Fuyun Ling, Marc Moonen, Ian K. Proudler, Chrysostomos L. Nikias, John G Proakis, et al, J. G. et al Proakis is a book available to read on EtoBox.

What is Algorithms For Statistical Signal Processing about?

Keeping pace with the expanding, ever more complex applications of DSP, this authoritative presentation of computational algorithms for statistical signal processing focuses on advanced topics ignored by other books on the subject. Algorithms for Convolution and DFT. Linear Prediction and Optimum Linear Filters. Least-Squares Methods for System Modeling and Filter Design. Adaptive Filters. Recursive Least-Squares Algorithms for Array Signal Processing. QRD-Based Fast Adaptive Filter Algorithms. Power Spectrum Estimation. Signal Analysis with Higher-Order Spectra. For Electrical Engineers, Computer Engineers, Computer Scientists, and Applied Mathematicians. Suitable for graduate-level courses in Digital Signal Processing in ECE and applied mathematics departments. This presentation of computational algorithms for statistical signal processing focuses on advanced topics, such as algorithms for adaptive filtering, least squares methods, power spectrum estimation, and high-order spectral estimation.

Author

Proakis, John G., Rader, Charles M., Ling, Fuyun, Moonen, Marc, Proudler, Ian K., Nikias, Chrysostomos L., John G. Proakis, Charles M. Rader, Fuyun Ling, Marc Moonen, Ian K. Proudler, Chrysostomos L. Nikias, John G Proakis, et al, J. G. et al Proakis

Publisher

Prentice Hall

Published

2002

Language

EN

ISBN

9780130622198

Subjects

Engineering, Mathematics, Computer Science

Rating

4.7 / 5 (2 ratings)

Updated

2026-03-14