Title :
A robust mixed-norm adaptive filter algorithm
Author :
Chambers, Jonathon ; Avlonitis, Apostolos
Author_Institution :
Signal Process. & Digital Syst. Sect., Imperial Coll. of Sci., Technol. & Med., London, UK
Abstract :
We propose a new member of the family of mixed-norm stochastic gradient adaptive filter algorithms for system identification applications based upon a convex function of the error norms that underlie the least mean square (LMS) and least absolute difference (LAD) algorithms. A scalar parameter controls the mixture and relates, approximately, to the probability that the instantaneous desired response of the adaptive filter does not contain significant impulsive noise. The parameter is calculated with the complementary error function and a robust estimate of the standard deviation of the desired response. The performance of the proposed algorithm is demonstrated in a system identification simulation with impulsive and Gaussian measurement noise.
Keywords :
Gaussian noise; adaptive filters; adaptive signal processing; error analysis; filtering theory; least mean squares methods; parameter estimation; probability; stochastic processes; Gaussian measurement noise; LMS algorithm; complementary error function; convex function; error norms; impulsive measurement noise; impulsive noise; least absolute difference algorithm; least mean squares; probability; robust estimate; robust mixed-norm adaptive filter algorithm; scalar parameter; standard deviation; stochastic gradient adaptive filter algorithms; system identification simulation; Adaptive filters; Additive noise; Finite impulse response filter; Gaussian noise; Least squares approximation; Noise measurement; Noise robustness; Signal processing algorithms; Stochastic resonance; System identification;
Journal_Title :
Signal Processing Letters, IEEE
