Title :
Least-mean kurtosis: a novel higher-order statistics based adaptive filtering algorithm
Author :
Tanrikulu, O. ; Constantinides, A.G.
Author_Institution :
Dept. of Electr. Eng., Imperial Coll. of Sci., Technol. & Med., London
fDate :
2/3/1994 12:00:00 AM
Abstract :
The least-mean kurtosis (LMK) adaptive FIR filtering algorithm is described which uses the negated kurtosis of the error signal as the cost function to be minimised. Unlike other higher-order statistics based adaptive algorithms, it is computationally efficient and it best suits those applications in which the noise contamination degrades the performance of the classical adaptive filtering algorithms
Keywords :
adaptive filters; computational complexity; filtering and prediction theory; interference (signal); least squares approximations; statistical analysis; computational complexity; cost function minimisation; error signal; higher-order statistics based adaptive algorithm; least-mean kurtosis adaptive FIR filtering algorithm; negated kurtosis; noise contamination degradation;
Journal_Title :
Electronics Letters
DOI :
10.1049/el:19940129
