Improved nonnegative adaptive filtering algorithms

The nonnegative least mean square (NNLMS) algorithm has the advantages of simplicity and ease of implementation, but it has a slow convergence rate in sparse nonnegative system identification and its robustness is not strong in an impulsive interference environment. To solve these problems, an lo-norm NNLMS (lo-NNLMS) algorithm is presented by using an lo-norm optimization. Then, an lo-norm nonnegative least logarithmic absolute difference (lo-NNLLAD) algorithm is proposed by combining the lo-norm of the weight vector and a logarithmic function as cost function. Simulation results show the lo-NNLMS algorithm can increase the convergence rate in sparse nonnegative system identification, and that the lo-NNLLAD algorithm is more robust than the lo-NNLMS algorithm.

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