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.

Algorithms for Statistical Signal Processing

Computers & Technology

Proakis, John G.

Ling, Fuyun

Rader, Charles M.

Moonen, Marc

Proudler, Ian K.

Nikias, Chrysostomos L.

Networking & Cloud Computing

PRENTICE/HALL

In response to the recent growth of interest in polyspectra, this timely text provides an introduction to signal processing methods that are based on polyspectra and cumulants concepts. The emphasis of the book is placed on the presentation of signal processing tools for use in situations where the more common power spectrum estimation techniques fall short. This manual will be valuable to practicing engineers who need an introduction to polyspectra from a signal processing perspective.

Higher-Order Spectra Analysis: A Nonlinear Signal Processing Framework

Petropulu, Athina P.

Software

Pearson College Div

The recent explosion in the speed and power of computers has now made available the much more accurate, non-Gaussian models in signal processing algorithms. This book offers, for the first time, a full and lucid introduction to a very useful type of these non-Gaussian models, namely those specified by alpha-stable distributions. 
Signal Processing with Alpha-Stable Distributions and Applications presents a long-awaited survey of the statistical properties, methods, and applications of symmetrical alpha-stable distributions. Its emphasis on practical rather than theoretical aspects will appeal to both researchers and practicing engineers in signal processing. Major topics covered in this comprehensive resource include: <ul>
<li>Statistical methods in signal processing, including both the Gaussian and alpha-stable models</li> <li>The stable distribution, its characterization and statistical properties</li> <li>Fractional lower-order moments</li> <li>Covariation and statistical conditional expectation of symmetric alpha-stable random variables</li> <li>Methods for estimating the parameters of a stable distribution from sample data</li> <li>Three conventional methods for estimating covariations: fractional lower-order moment estimator, the screened ratio estimator, and the least-squares estimator</li> <li>Methods for estimating MA, AR, and ARMA model parameters</li> <li>Methods for modeling impulsive signals</li> <li>Designing and implementing optimum and suboptimum signal detectors in the presence of impulsive noise</li> <li>Overview of current applications and future research trends of alpha-stable distributions for signal processing problems</li> </ul>

Signal Processing with Alpha-Stable Distributions and Applications (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control)

Shao, Min

Wiley-Interscience

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