Robustness of Kernel Based Regression: A Comparison of Iterative Weighting Schemes
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Improving PSK performance in snapping shrimp noise with rotated constellations
Proceedings of the Seventh ACM International Conference on Underwater Networks and Systems
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We introduce a robust and nonlinear filtering framework: weighted myriad filtering. Much like the Gaussian assumption has motivated the development of linear filtering theory, the formulation of myriad filters is motivated by the statistical properties of /spl alpha/-stable processes. Weighted myriad filters have a solid theoretical basis, are inherently more powerful than weighted median filters, and are very general subsuming traditional linear FIR filters. The foundation of the proposed filtering algorithms lies in the definition of the sample myriad as the location estimate for a class of /spl alpha/-stable distributions. In turn, the myriad has been discovered as the location parameter estimated by the sample myriad. This paper addresses some theoretical properties of myriad filters. The superior performance of myriad filters in impulsive environments is illustrated in the problem of robust synchronization by means of a "myriad phase lock loop".