Abstract— This paper proposes an improved least mean kurtosis (LMK) algorithm based on l0-norm cost for enhancing the filter performance in a sparse system. The LMK adaptive filtering algorithm uses a kurtosis of an estimated error signal to improve the filter performance when the noise contamination is serious. Due to the influence of l0-norm cost, the proposed LMK algorithm ensures a fast convergence rate and a small steady-state error in sparse system environment. Simulation results verify that the proposed algorithm improves the filter performance for sparse system identification.
Index Terms— Adaptive filter, least mean kurtosis algorithm, sparse system identification.
Jin Woo Yoo is with the Department of Electrical Engineering, Pohang University of Science and Technology, San 31, Hyojadong, Namgu, Pohang, Kyungbuk, 790-784, Korea (e-mail: spinkey@postech.ac.kr).
Poo Gyeon Park is with the Division of IT Convergence Engineering and the Department of Electrical Engineering, Pohang University of Science and Technology, San 31, Hyojadong, Namgu, Pohang, Kyungbuk, 790-784, Korea (e-mail: ppg@postech.ac.kr).
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Cite: Jin Woo Yoo and Poo Gyeon Park, "An Improved Least Mean Kurtosis (LMK) Algorithm for Sparse System Identification," International Journal of Information and Electronics Engineering vol. 2, no. 6, pp. 940-943, 2012.
