Adaptive Filtering: Applications, Algorithms, New Results, and Open Issues

Eweda Eweda, Fellow IEEE

Head of the EE Department, Ajman University, UAE

ABSTRACT: Adaptive filtering is an important topic that has applications in several engineering areas such as communications, control, and biomedical engineering. Examples of such applications are adaptive noise canceling, adaptive equalization of data transmission channels, adaptive antenna arrays, adaptive system identification, and adaptive canceling of narrowband interference in direct sequence spread spectrum systems. Adaptive filtering is made of a digital filter whose weights are controlled by an adaptation algorithm so as to minimize the difference between the filter output and a reference signal according to some criterion. The nature of the reference signal depends on the considered application. There are two main measures for evaluating the performance of an adaptive filter: the convergence rate and the steady state mean square error. In practical applications, it is desired to maximize the convergence rate and minimize the steady state mean square error. There is a conflict between these requirements. Several adaptation algorithms have been developed so as to yield a good compromise between these requirements. Improving this compromise is a continuous open issue. This presentation gives a survey of main applications of adaptive filtering, performance criteria, adaptation algorithms, some new results attained by the presenter, and open issues.