Multichannel joint estimation via structured sparse array

When we do multichannel estimation，if the sparsity and relevance of multichannel are used, the performance of channel estimation can be improved. This paper proposes a Group Sparse LMS algorithm which is based on structural sparse priori of the array channel. The algorithm takes these channels as a whole for adaptive channel estimation, a gradient descent recursion of the filter coefficient vector is deduced through introducing Ι_2,1norm, which introduce structural sparse priori to the criterion function of sparse LMS algorithm. The simulation results show that, under different path conditions, steady state error performance of the proposed algorithm is obviously better than several existing sparse LMS algorithms.