By Stergios Stergiopoulos

ISBN-10: 0849336910

ISBN-13: 9780849336911

Advances in electronic sign processing algorithms and desktop know-how have mixed to provide real-time platforms with functions some distance past these of simply few years in the past. Nonlinear, adaptive tools for sign processing have emerged to supply higher array achieve functionality, despite the fact that, they lack the robustness of traditional algorithms. The problem continues to be to increase an idea that exploits some great benefits of both-a scheme that integrates those equipment in useful, real-time systems.The complicated sign Processing guide is helping you meet that problem. past providing a superb advent to the foundations and functions of complex sign processing, it develops a familiar processing constitution that takes benefit of the similarities that exist between radar, sonar, and scientific imaging platforms and integrates traditional and nonlinear processing schemes.

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**Extra resources for Advanced Signal Processing Handbook**

**Sample text**

Otherwise, the adaptive filter is said to be nonlinear. A wide variety of recursive algorithms have been developed in the literature of the operation of linear adaptive filters. In the final analysis, the choice of one algorithm over another is determined by one or more of the following factors: • Rate of convergence — This is defined as the number of iterations required for the algorithm, in response to stationary inputs, to converge “close enough” to the optimum Wiener solution in the mean-square sense.

2001 CRC Press LLC optimization theory. This modification requires the use of a gradient vector, the value of which depends on two parameters: the correlation matrix of the tap inputs in the transversal filter and the cross-correlation vector between the desired response and the same tap inputs. Next, we use instantaneous values for these correlations so as to derive an estimate for the gradient vector, making it assume a stochastic character in general. The resulting algorithm is widely known as the least-mean-square (LMS) algorithm, the essence of which may be described in words as follows for the case of a transversal filter operating on real-valued data: updated value old value learning- tap- error of tap-weight = of tap-weight + rate input signal vector parameter vector vector where the error signal is defined as the difference between some desired response and the actual response of the transversal filter produced by the tap-input vector.

Notwithstanding this property, adaptive filters are commonly classified as linear or nonlinear. An adaptive filter is said to be linear if the estimate of quantity of interest is computed adaptively (at the output of the filter) as a linear combination of the available set of observations applied to the filter input. Otherwise, the adaptive filter is said to be nonlinear. A wide variety of recursive algorithms have been developed in the literature of the operation of linear adaptive filters. In the final analysis, the choice of one algorithm over another is determined by one or more of the following factors: • Rate of convergence — This is defined as the number of iterations required for the algorithm, in response to stationary inputs, to converge “close enough” to the optimum Wiener solution in the mean-square sense.

### Advanced Signal Processing Handbook by Stergios Stergiopoulos

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