An adaptive filter is a signal processing device that is self-designing in that the adaptive filter relies for its operation on an iterative algorithm. The algorithm makes it possible for the filter to perform satisfactorily in an environment when complete knowledge of the relevant signal characteristics is not available [1], Adaptive filters find applications in such diverse fields as communications, radar, sonar, control, and biomedical engineering.
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A novel filtering algorithm called adaptive directional weighted median (ADWM) filtering is proposed in this paper. The ideas of the directional filtering and the weighted median filtering are combined to construct the ADWM filter. The use of the variance of the moving window and the base variance support the adaptivity of the ADWM filter. The experimental results show that the ADWM filter can both reduce random noise and preserve details.
This paper develops a unified approach to the analysis and design of adaptive filters with error nonlinearities. In particular, the paper performs stability and steady-state analysis of this class of filters under weaker conditions than what is usually encountered in the literature, and without imposing any restriction on the color or statistics of the input. The analysis results are subsequently used to derive an expression for the optimum nonlinearity, which turns out to be a function of the probability density function of the estimation error. Some common nonlinearities are shown to be approximations to the optimum nonlinearity. The framework pursued here is based on energy conservation arguments. 2ff7e9595c
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