Robust least mean mixed norm adaptive filtering for α-stable random processes
Proceedings of the 1997 IEEE International Symposium on Circuits and Systems
2296 - 2299
Item Usage Stats
Based on the concept of Fractional Lower Order Statistics (FLOS), we present the Robust Least Mean Mixed Norm (RLMMN) adaptive algorithm for applications in impulsive environments modeled by α-stable distributions. A sufficient condition for finite variance of the update term is obtained for the underlying α-stable process. Simulation results are provided regarding the identification of the parameters of an AR system.
Spurious signal noise
Robust least mean mixed norm adaptive algorithm
Robust least mean mixed norm adaptive filtering
Published Version (Please cite this version)https://www.doi.org/10.1109/ISCAS.1997.612781
Showing items related by title, author, creator and subject.
Kari, Dariush; Marivani, Iman; Delibalta, İ.; Kozat, Süleyman Serdar (IEEE, 2016)We introduce the boosting notion extensively used in different machine learning applications to adaptive signal processing literature and implement several different adaptive filtering algorithms. In this framework, we ...
Sayın, Muhammed O.; Vanlı, N. Denizcan; Kozat, Süleyman S. (IEEE, 2014-05)We present a novel family of adaptive filtering algorithms based on a relative logarithmic cost. The new family intrinsically combines the higher and lower order measures of the error into a single continuous update based ...
Uyanık, İsmail; Saranlı, Uluç; Morgül, Ömer (IEEE, 2011)Practical realization of model-based dynamic legged behaviors is substantially more challenging than statically stable behaviors due to their heavy dependence on second-order system dynamics. This problem is further ...