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Microstatistic LMS filtering

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2 Author(s)
Chen, S. ; Appl. Sci. & Eng. Center, Delaware Univ., Wilmington, DE, USA ; Arce, G.R.

Adaptive microstatistic filters are developed for applications in which the second-order statistics of the thresholded signals are not known or may be nonstationary. A multilevel threshold decomposition such that real-valued stochastic processes can be filtered is used, and the computational complexity of the algorithm can be arbitrarily specified by the designer. The adaptation uses the least-mean-squares error approach of the least-mean-square (LMS) algorithm. The convergence of the adaptive algorithm is proved. Due to the nonhomogeneous statistical characteristic of the threshold signals, a different step-size adaptation parameter can be assigned to each threshold level. Simple design guidelines are developed for finding the set of nonhomogeneous step sizes which in practice yield better convergence characteristics

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Signal Processing, IEEE Transactions on  (Volume:41 ,  Issue: 3 )