The Least Mean Kurtosis: Its Steady State Performance with Gaussian and Non-Gaussian Noise

J. Sanubari (Indonesia)


Steady state analysis, least mean kurtosis, A robust adap tive filter.


In this paper, the final mean square error (MSE) of the least mean kurtosis (LMK) adaptive algorithm is theoretically derived by applying the energy conservation adaptive filters and on the n-th order correlation theory. The behavior is compared with the conventional least mean square (LMS) algorithm. Our study shows that it is possible to adjust the performance of the LMK. It can even outperform the LMS.

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