Statistically-Efficient Filtering in Impulsive Environments: Weighted Myriad Filters

• Main Authors: Gonzalez Juan G, Arce Gonzalo R
• Format: Article
• Language: English
• Published: SpringerOpen 2002-01-01
• Series: EURASIP Journal on Advances in Signal Processing
• Subjects: weighted myriad filters; weighted median filters; impulsive noise; heavy tails; alpha-stable distributions; Cauchy distribution; phase-locked loop
• Online Access: http://dx.doi.org/10.1155/S1110865702000483

<p/> <p>Linear filtering theory has been largely motivated by the characteristics of Gaussian signals. In the same manner, the proposed <it>Myriad Filtering</it> methods are motivated by the need for a flexible filter class with high statistical efficiency in non-Gaussian impulsive environments that can appear in practice. Myriad filters have a solid theoretical basis, are inherently more powerful than median filters, and are very general, subsuming traditional linear FIR filters. The foundation of the proposed filtering algorithms lies in the definition of the <it>myriad</it> as a tunable estimator of location derived from the theory of robust statistics. We prove several fundamental properties of this estimator and show its optimality in practical impulsive models such as the <inline-formula><graphic file="1687-6180-2002-363195-i1.gif"/></inline-formula>-<it>stable</it> and <it>generalized</it>- <inline-formula><graphic file="1687-6180-2002-363195-i2.gif"/></inline-formula>. We then extend the myriad estimation framework to allow the use of weights. In the same way as linear FIR filters become a powerful generalization of the mean filter, filters based on running myriads reach all of their potential when a weighting scheme is utilized. We derive the "normal" equations for the optimal myriad filter, and introduce a suboptimal methodology for filter tuning and design. The strong potential of myriad filtering and estimation in impulsive environments is illustrated with several examples.</p>