Search

The complex domain adaptive filtering algorithms has shown excellent performance in the field of signal processing. Among them, the well-known complex least mean square (CLMS) algorithm has been widely used in practical projects. However, the CLMS algorithm only considers the case where the output signal is disturbed by noise, ignores the input signal may also be interfered by noise. Moreover, the signal is likely to be disturbed by impulsive noise in practice. Based on the above problems, the complex total least mean M-estimation (CTLMM) adaptive algorithm based on the M-estimation function is put forward in this paper. The algorithm can well handle the case where both input and output signals are subject to complex domain noise interference, and it also shows robustness with impulsive noise. This paper also analyzes the local stability of the CTLMM algorithm and discuss the theoretical steady-state error of the algorithm. Finally, simulation results verify the advantages of CTLMM algorithm and the rightness of the theoretical analysis. [ABSTRACT FROM AUTHOR]

To ameliorate the performance of the normalized least mean square (NLMS) algorithm applied in identifying block-sparse system, a novel block-sparse proportionate NLMS (BS-PNLMS) algorithm based on the mixed l 2,0 norm is presented in this paper. The cost function of proposed algorithm is obtained by introducing the approximated mixed l 2,0 norm penalty in the cost function of BS-PNLMS algorithm, which is equivalent to attaching the mixed l 2,0 norm zero attraction to the update equation. The proposed algorithm is named as the l 2,0 -BS-PNLMS algorithm. Futhermore, a variable step-size l 2,0 -BS-PNLMS is developed to further improve the performance of the l 2,0 -BS-PNLMS. Simulation results demonstrate the advantages of proposed algorithms. [ABSTRACT FROM AUTHOR]