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.
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