By Stergios Stergiopoulos
Advances in electronic sign processing algorithms and computing device 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 higher array achieve functionality, in spite of the fact that, they lack the robustness of traditional algorithms. The problem continues to be to improve an idea that exploits some great benefits of both-a scheme that integrates those equipment in functional, real-time systems.
The complicated sign Processing guide is helping you meet that problem. past providing an excellent advent to the foundations and purposes of complicated sign processing, it develops a regular processing constitution that takes good thing about the similarities that exist between radar, sonar, and clinical imaging structures and integrates traditional and nonlinear processing schemes
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Extra info for Advanced signal processing handbook : theory and implementation for radar, sonar, and medical imaging real time systems
The use of computer simulation provides a good first step in undertaking a detailed investigation of these issues. We may begin by using the LMS algorithm as an adaptive filtering tool for the study. The LMS algorithm is relatively simple to implement. Yet it is powerful enough to evaluate the practical benefits that may result from the application of adaptivity to the problem at hand. Moreover, it provides a practical frame of reference for assessing any further improvement that may be attained through the use of more sophisticated adaptive filtering algorithms.
We may classify the RLS family of linear adaptive filtering algorithms into three distinct categories, depending on the approach taken: 1. Standard RLS algorithm assumes the use of a transversal filter as the structural basis of the linear adaptive filter. 2 summarizes the standard RLS algorithm. Derivation of this algorithm relies on a basic result in linear algebra known as the matrix inversion lemma. Most importantly, it enjoys the same virtues and suffers from the same limitations as the standard Kalman filtering algorithm.
By concurrency, we mean a large number of similar computations being performed at the same time. For a discussion of VLSI implementation of adaptive filters, see Shabhag and Parhi (1994). This book emphasizes the use of pipelining, an architectural technique used for increasing the throughput of an adaptive filtering algorithm. ©2001 CRC Press LLC of concern: numerical stability and numerical accuracy. Numerical stability is an inherent characteristic of an adaptive filtering algorithm. , binary digits used in the numerical representation of data samples and filter coefficients).