Improved Constraint NLMS Algorithm for Sparse Adaptive Array Beamforming Control Applications

Authors

  • Wanlu Shi College of Information and Communication Engineering Harbin Engineering University, Harbin, 150001, China
  • Yingsong Li 1 College of Information and Communication Engineering Harbin Engineering University, Harbin, 150001, China ,2 Key Laboratory of Microwave Remote Sensing National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China
  • Jingwei Yin Acoustic Science and Technology Laboratory Harbin Engineering University, Harbin, 150001, China

Keywords:

array beamforming, constrained LMS algorithm, l1-norm constraint, lp-norm constraint, sparse adaptive beamforming

Abstract

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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Published

2019-03-01

How to Cite

[1]
Wanlu Shi, Yingsong Li, and Jingwei Yin, “Improved Constraint NLMS Algorithm for Sparse Adaptive Array Beamforming Control Applications”, ACES Journal, vol. 34, no. 03, pp. 419–423, Mar. 2019.

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Articles