Adaptive Sparse Array Beamforming Using Correntropy Induced Metric Constrained Normalized LMS Algorithm

Authors

  • Zehua Dai Acoustic Science and Technology Laboratory Harbin Engineering University, Harbin 150001, China , Key Laboratory of Marine Information Acquisition and Security, Harbin Engineering University Ministry of Industry and Information Technology, Harbin 150001, China
  • Longxiang Guo Acoustic Science and Technology Laboratory Harbin Engineering University, Harbin 150001, China , Key Laboratory of Marine Information Acquisition and Security, Harbin Engineering University Ministry of Industry and Information Technology, Harbin 150001, China
  • Jingwei Yin Acoustic Science and Technology Laboratory Harbin Engineering University, Harbin 150001, China ,Key Laboratory of Marine Information Acquisition and Security, Harbin Engineering
  • Yingsong Li College of Information and Communication Engineering Harbin Engineering University, Harbin 150001, China
  • Kun Guo Acoustic Science and Technology Laboratory Harbin Engineering University, Harbin 150001, China, Key Laboratory of Marine Information Acquisition and Security, Harbin Engineering University Ministry of Industry and Information Technology, Harbin 150001, China

Keywords:

adaptive array beamforming, CNLMS algorithm, correntropy induced metric, , sparse arrays

Abstract

In order to further exploit the sparseness of antenna array and speed up the convergence of constrained normalized LMS (CNLMS) algorithm, maintaining good beam pattern performance and better output signal-to-interferences-plus-noise ratio (SINR), a new method with approximation l0-norm constraint is proposed to improve CNLMS algorithm, and its derivation process is given in detail. In this newly proposed algorithm, the correntropy induced metric (CIM) is used to approximate the l0-norm, which is considered construct a new cost function to fully exploit the sparsity of the antenna array and reduced the number of active array elements. Using the CIM penalty, the proposed CIM-based CNLMS (CIM-CNLMS) algorithm is derived in detail, where the Lagrange multiplier method is utilized to solve the cost function of the proposed CIM-CNLMS algorithm, and the steepest descent principle is considered to obtain the update equation. The computer simulation results demonstrate that compared with other CLMS algorithms, the new algorithm obtains better performance, which greatly reduces the proportion of active array elements in the thinned antenna array. Simultaneously, the new algorithm has excellent beam pattern performance and better SINR performance with faster convergence speed and more stable mean square error.

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References

J. Li and P. Stoica (Eds.), Robust Adaptive Beamforming. John Wiley & Sons, New York, NY, 2005.

H. L. Van, Trees, Detection, Estimation, and Modulation Theory, Part IV: Optimum Array Processing. John Wiley & Sons, New York, NY, 2002.

O. L. Frost III, “An algorithm for linearly constrained adaptive array processing,” Proc. IEEE, vol. 60, no. 8, pp. 926-935, Aug. 1972.

J. A. Apolinário, S. Werner, P. S. R. Diniz, and T. I. Laakso, “Constrained normalized adaptive filtering for CDMA mobile communications,” IEEE Signal Processing Conference, Rhodes, Greece, pp. 1-4, Sep. 1998.

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.

Y. Chen, Y. Gu, and A. O. Hero, “Sparse LMS for system identification,” IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan, pp. 3125-3128, Apr. 2009.

O. Taheri and S. A. Vorobyov, “Sparse channel estimation with

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Published

2020-04-01

How to Cite

[1]
Zehua Dai, Longxiang Guo, Jingwei Yin, Yingsong Li, and Kun Guo, “Adaptive Sparse Array Beamforming Using Correntropy Induced Metric Constrained Normalized LMS Algorithm”, ACES Journal, vol. 35, no. 4, pp. 430–436, Apr. 2020.

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Articles