A robust mixed-norm adaptive filter algorithm
journal contributionposted on 22.01.2010 by Jonathon Chambers, Apostolos Avlonitis
Any type of content formally published in an academic journal, usually following a peer-review process.
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
- Mechanical, Electrical and Manufacturing Engineering