Advanced Signal Processing Handbook: Theory and by Stergios Stergiopoulos

By Stergios Stergiopoulos

Advances in electronic sign processing algorithms and laptop expertise have mixed to supply real-time structures with services a long way past these of simply few years in the past. Nonlinear, adaptive equipment for sign processing have emerged to supply larger array achieve functionality, despite the fact that, they lack the robustness of traditional algorithms. The problem is still to increase an idea that exploits some great benefits of both-a scheme that integrates those tools in useful, real-time systems.

The complicated sign Processing guide is helping you meet that problem. past providing a great advent to the rules and purposes of complex sign processing, it develops a everyday processing constitution that takes good thing about the similarities that exist between radar, sonar, and clinical imaging platforms and integrates traditional and nonlinear processing schemes.

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Additional resources for Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real Time Systems (Electrical Engineering & Applied Signal Processing Series)

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This means that in the case of smoothing there is a delay in producing the result of interest. Since * The material presented in this chapter is based on the author’s two textbooks: (1) Adaptive Filter Theory (1996) and (2) Neural Networks: A Comprehensive Foundation (1999), Prentice-Hall, Englewood Cliffs, NJ. ©2001 CRC Press LLC in the smoothing process we are able to use data obtained not only up to time t, but also data obtained after time t, we would expect smoothing to be more accurate in some sense than filtering.

The disturbances may arise from a variety of factors internal or external to the filter. , multiplications, divisions, and additions/subtractions) required to make one complete iteration of the algorithm, (2) the size of memory locations required to store the data and the program, and (3) the investment required to program the algorithm on a computer. • Structure — This refers to the structure of information flow in the algorithm, determining the manner in which it is implemented in hardware form.

Two types of fast RLS algorithms may be identified, depending on the filtering structure employed: • Order-recursive adaptive filters, which are based on a lattice-like structure for making linear forward and backward predictions • Fast transversal filters, in which the linear forward and backward predictions are performed using separate transversal filters Certain (but not all) realizations of order-recursive adaptive filters are known to be numerically stable, whereas fast transversal filters suffer from a numerical stability problem and, therefore, require some form of stabilization for them to be of practical use.

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