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.

Downloads

Download data is not yet available.

References

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

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

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, Jr., S. Werner, P. S. R. Diniz, and T. I. Laakso, “Constrained normalized adaptive filtering for CDMA mobile communications,” IEEE Signal Processing Conference, Rhodes, Greece, Sep. 1998.

Y. Chen, Y. Gu, and A. O. Hero, “Sparse LMS for system identification,” Proc. IEEE International Conference on Acoustic Speech and Signal Processing, (ICASSP’09), Taipei, Taiwan, pp. 3125- 3128, Apr. 2009.

O. Taheri and S. A. Vorobyov, “Sparse channel estimation with Lp-norm and reweighted L1-norm penalized least mean squares,” IEEE International Conference on Acoustic Speech and Signal Processing (ICASSP’11), Prague, Czech Republic, pp. 2864-2867, May 2011.

Y. Wang, Y. Li, and Z. Jin, “An improved reweighted zero-attracting NLMS algorithm for broadband sparse channel estimation,” IEEE International Conference on Electronic Information and Communication Technology, Harbin, China, Aug. 2016.

Y. Gu, J. Jin, and S. Mei, “l0-norm constraint LMS algorithm for sparse system identification,” IEEE Signal Process. Lett., vol. 16, no. 9, pp. 774-777, Sept. 2009. 10.1109/LSP.2009.2024736.

Y. Li, Y. Wang, R. Yang, et al., “A soft parameter function penalized normalized maximum correntropy criterion algorithm for sparse system identification,” Entropy, vol. 19, no. 1, p. 45, Jan. 2017. 10.3390/e19010045.

Z. Jin, Y. Li, and J. Liu, “An improved setmembership proportionate adaptive algorithm for a block-sparse system,” Symmetry, vol. 10, no. 3, p. 75, Mar. 2018. 10.3390/sym10030075.

D. Angelosante, J. A. Bazerque, and G. B. Giannakis, “Online adaptive estimation of sparse signals: Where RLS meets the l1-norm,” IEEE Transactions on Signal Processing, vol. 58, no. 7, pp. 3436-3447, Mar. 2010. 10.1109/TSP.2010. 2046897.

O. Taheri and S. A. Vorobyov, “Reweighted l1- norm penalized LMS for sparse channel estimation and its analysis,” Elsevier Signal Processing, vol. 104, pp. 70-79, May 2014.

Y. Li, Y. Wang, and T. Jiang, “Sparse-aware set-membership NLMS algorithms and their application for sparse channel estimation and echo cancelation,” AEUE - International Journal of Electronics and Communications, vol. 70, no. 7, pp. 895-902, 2016.

Y. Li, Y. Wang, and T. Jiang, “Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation,” Signal Processing, 128:243-251, 2016.

R. Tibshirani, “Regression shrinkage and selection via the lasso,” J. R. Stat. Soc. Ser. B-Stat. Methodol., vol. 58, no. 1, pp. 267-288, Jan. 1996.

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

J. F. de Andrade, M. L. R. de Campos, and J. A. Apolinário, “L1-constrained normalized LMS algorithms for adaptive beamforming,” IEEE Transactions on Signal Processing, vol. 63, no. 24, pp. 6524-6539, Dec. 2015. 10.1109/TSP.2015.2474302.

W. Shi, Y. Li, and S. Luo, “Adaptive antenna array beamforming based on norm penalized NLMS algorithm,” 2018 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, in press, Boston, America, July 2018.

W. Shi and Y. Li, “Norm-constrained NLMS for sparse controllable adaptive array beamforming,” 2018 International Applied Computational Electromagnetics Society Symposium, in press, Beijing, China, July 2018.

P. S. R. Diniz, Adaptive Filtering: Algorithms and Practical Implementation. New York, USA: Springer, 2010.

K. Yu, Y. Li, and X. Liu, “Mutual coupling reduction of a MIMO antenna array using 3-D novel meta-material structures,” Applied Computational Electromagnetics Society Journal, vol. 33, no. 7, pp. 758-763, 2018.

T. Jiang, T. Jiao, Y. Li, and W. Yu, “A low mutual coupling MIMO antenna using periodic multilayered electromagnetic band gap structures,” Applied Computational Electromagnetics Society Journal, vol. 33, no. 3, pp. 305-311, 2018.

Y. Li, W. Li, and W. Yu, “A multi-band/UWB MIMO/diversity antenna with an enhance isolation using radial stub loaded resonator,” Applied Computational Electromagnetics Society Journal, vol. 28, no. 1, pp. 8-20.

Downloads

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.

Issue

Section

General Submission