An Improved Least Mean Kurtosis ( LMK ) Algorithm for Sparse System Identification
نویسنده
چکیده
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.
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