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Automatic Autocorrelation and Spectral Analysis by Petrus M. T. Broersen is a nonfiction available to read on EtoBox.

What is Automatic Autocorrelation and Spectral Analysis about?

Automatic Autocorrelation and Spectral Analysis gives random data a language to communicate the information they contain objectively. In the current practice of spectral analysis, subjective decisions have to be made all of which influence the final spectral estimate and mean that different analysts obtain different results from the same stationary stochastic observations. Statistical signal processing can overcome this difficulty, producing a unique solution for any set of observations but that solution is only acceptable if it is close to the best attainable accuracy for most types of stationary data. Automatic Autocorrelation and Spectral Analysis describes a method which fulfils the near-optimal-solution criterion. It takes advantage of greater computing power and robust algorithms to produce enough models to be sure of providing a suitable candidate for given data. Improved order selection quality guarantees that one of the best (and often the best) will be selected automatically. The data themselves suggest their best representation but should the analyst wish to intervene, alternatives can be provided. Written for graduate signal processing students and for researchers and e

Who reads Automatic Autocorrelation and Spectral Analysis?

Author

Petrus M. T. Broersen

Publisher

Springer London : Imprint: Springer

Published

2006

Language

EN

ISBN

9786611328931

Category

nonfiction

Subjects

Technology, Science, Chemistry