Robust adaptive filtering algorithms for α-stable random processes
Çetin, A. E.
IEEE Transactions on Circuits and Systems II : Analog and Digital Signal Processing
Institute of Electrical and Electronics Engineers
198 - 202
Item Usage Stats
MetadataShow full item record
A new class of algorithms based on the fractional lower order statistics is proposed for finite-impulse response adaptive filtering in the presence of α-stable processes. It is shown that the normalized least mean p-norm (NLMP) and Douglas' family of normalized least mean square algorithms are special cases of the proposed class of algorithms. A convergence proof for the new algorithm is given by showing that it performs a descent-type update of the NLMP cost function. Simulation studies indicate that the proposed algorithms provide superior performance in impulsive noise environments compared to the existing approaches.
Probability density function
Spurious signal noise
Alpha stable random processes
Finite impulse response adaptive filtering
Least mean square algorithms
Normalized least mean p norm