Improved Constraint NLMS Algorithm for Sparse Adaptive Array Beamforming Control Applications
Keywords:
array beamforming, constrained LMS algorithm, l1-norm constraint, lp-norm constraint, sparse adaptive beamformingAbstract
In this paper, a new reweighted l1-norm and an lp-norm based normalized least mean square (NLMS) algorithms are developed for sparse adaptive array beamforming control applications. The proposed reweighted l1-norm constrained NLMS (RL1-CNLMS) and lp-norm constrained NLMS (LP-CNLMS) algorithms use the l1-norm penalty and lp-norm penalty to the conventional cost function of constrained normalized LMS (CLMS) algorithm to control the sparsity of the antenna array. What’s more, in the derivation process, the gradient descent principle and Lagrange multiplier method are adopted to obtain the desired updating formulations. Computer simulations demonstrate that the superiority of proposed algorithms compared with other LMS based beamforming methods.
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