Analysis of polynomial FM signals corrupted by heavy-tailed noise
Signal Processing
New polynomial approach to myriad filter computation
Signal Processing
Robust fuzzy clustering using adaptive fuzzy meridians
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Hybrid fuzzy clustering using LP norms
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Impulsive noise cancelation with simplified Cauchy-based p-norm filter
Signal Processing
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This paper develops fast algorithms to compute the output of the weighted myriad filter. Myriad filters form a large and important class of nonlinear filters for robust non-Gaussian signal processing and communications in impulsive noise environments. Just as the weighted mean and the weighted median are optimized for the Gaussian and Laplacian distributions, respectively, the weighted myriad is based on the class of α-stable distributions, which can accurately model impulsive processes. The weighted myriad is an M-estimator that is defined in an implicit manner; no closed-form expression exists for it, and its direct computation is a nontrivial and prohibitively expensive task. In this paper, the weighted myriad is formulated as one of the fixed points of a certain mapping. An iterative algorithm is proposed to compute these fixed points, and its convergence is proved rigorously. Using these fixed point iterations, fast algorithms are developed for the weighted myriad. Numerical simulations demonstrate that these algorithms compute the weighted myriad with a high degree of accuracy at a relatively low computational cost