Robust Adaptive Beamforming Based on Fuzzy Cerebellar Model Articulation Controller Neural Network
Keywords:
Adaptive beamforming, FCMAC, neural network, robustness, steering vector mismatchAbstract
To solve the problem of degraded adaptive beamforming performance of smart antenna caused by array steering vector mismatch and array manifold errors, a robust beamforming algorithm based on Fuzzy Cerebellar Model Articulation Controller (FCMAC) neural network is proposed. The proposed algorithm is based on explicit modeling of uncertainties in the desired signal array response and a FCMAC neural network. The calculation of the optimal weight vector is viewed as a mapping problem, which can be solved using FCMAC neural network trained with input/output pairs. Our proposed approach provides excellent robustness against some types of mismatches and keeps the mean output array SINR consistently close to the optimal value. Moreover, the FCMAC neural network avoids complex matrix inversion operations and offers fast convergence rate. Simulation results show that the proposed algorithm can significantly enhance the robustness of the beamformer in the presence of array steering vector mismatch and array manifold errors, and the output performance is superior to the current methods.
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W. Li, X. Mao, Z. Zai, and Y. Li, “A low complexity high performance robust adaptive beamforming,” Applied Computational Electromagnetics Society, vol. 32, no. 5, pp. 441-448, 2017.
W. M. Jia, W. Jin, S. H. Zhou, and M. L. Yao, “Robust adaptive beamforming based on a new steering vector estimation algorithm,” Signal Processing, vol. 93, no. 9, pp. 2539-2542, 2013.
J. Capon, “High-resolution frequency-wave -number spectrum analysis,” Proc. IEEE, vol. 57, no. 8, pp. 1408-1418, 1969.
C. F. Liu and G. S. Liao, “Robust beamforming algorithm using worse-case performance optimization,” Journal of Xidian University (Natural Science), vol. 37, no. 1, pp. 1-7, 2010.
Z. L. Yu, “Robust adaptive beamformers based on worst-case optimization and constraints on magnitude response,” IEEE Transactions on Signal Processing, vol. 57, no. 7, pp. 2615-2628, 2009.
J. A. Srar, K. S. Chung, and A. Mansour, “Adaptive array beamforming using a combined LMS-LMS algorithm,” IEEE Transactions on Antennas and Propagation, vol. 58, no. 11, pp. 3545-3557, 2010.
S. D. Somasundaram, “Linearly constrained robust Capon beamforming,” IEEE Transactions on Signal Processing, vol. 60, no. 11, pp. 5845-5856, 2012.
J. H. Qian, Z. S. He, and T. Liu, “Robust beamforming based on steering vector and covariance matrix estimation,” Circuits, Systems, and Signal Processing, vol. 37, no. 10, pp. 4665- 4682, 2018.
Z. H. Li, Y. S. Zhang, and H. W. Liu, “A robust STAP method for airborne radar based on clutter covariance matrix reconstruction and steering vector estimation,” Digital Signal Processing, vol. 78, no. 2, pp. 82-91, 2018.
F. G. Yan, B. Cao, and J. J. Rong, “Spatial aliasing for efficient direction-of-arrival estimation based on steering vector reconstruction,”EURASIP Journal on Advances in Signal Processing, vol. 1, p. 121, 2016.
Y. J. Gu, N. A. Goodman, and S. H. Hong, “Robust adaptive beamforming based on interference covariance matrix sparse reconstruction,” Signal Processing, vol. 96, no. 5, pp. 375-381, 2014.
C. W. Zhou, Y. J. Gu, S. He, and Z. Shi, “A robust and efficient algorithm for coprime array adaptive beamforming,” IEEE Transactions on Vehicular Technology, vol. 67, no. 2, pp. 1099-1112, 2018.
Z. Y. Zhang, W. Liu, W. Leng, A. G. Wang, and H. P. Shi, “Interference-plus-noise covariance matrix reconstruction via spatial power spectrum sampling for robust adaptive beamforming,” IEEE Signal Processing Letters, vol. 23, no. 1, pp. 121-125, 2016.
L. Huang, J. Zhang, X. Xu, and Z. F. Ye, “Robust adaptive beamforming with a novel interferenceplus-noise covariance matrix reconstruction method,” IEEE Transactions on Signal Processing, vol. 63, no. 7, pp. 1643-1650, 2015.
X. Yuan and L. Gan, “Robust adaptive beamforming via a novel subspace method for interference covariance matrix reconstruction,” Signal Process, vol. 130, pp. 233-242, 2017.
W. X. Li, Z. Yu, Q. B. Ye, and S. Li, “An improved null broadening beamforming method based on covariance matrix reconstruction,” Applied Computational Electromagnetics Society, vol. 32, no. 2, pp. 128-134, 2017.
W. H. Orozco, P. M. Hector, and N. M. Mariko, “A new step-size searching algorithm based on fuzzy logic and neural networks for LMS adaptive beamforming systems,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 24, no. 7, pp. 4322-4338, 2016.
Z. D. Zaharis, C. Skeberis, and T. D. Xenos, “Design of a novel antenna array beamformer using neural networks trained by modified adaptive dispersion invasive weed optimization based data,” IEEE Transactions on Broadcasting, vol. 59, no. 3, pp. 455-460, 2013.
H. J. Che, C. Li, and X. He, “A recurrent neural network for adaptive beamforming and array correction,” Neural Networks, vol. 80, no. C, pp. 110- 117, 2016.
R. Savitha, S. Vigneswaran, and S. Suresh, “Adaptive beamforming using complex-valued radial basis function neural networks,” TENCON 2009 - 2009 IEEE Region 10 Conference IEEE, vol. 34, pp. 1-6, 2009.
A. H. Sallomi and S. Ahmed, “Multi-layer feed forward neural network application in adaptive beamforming of smart antenna system,” Al-Sadeq International Conference on Multidisciplinary in It and Communication Science and Applications IEEE, 2016.
T. Higuchi, K. Kinoshita, and N. Ito, “Frame-byframe closed-form update for mask-based adaptive MVDR beamforming,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
J. S. Albus, “A new approach to manipulator control: The cerebellar model articulation controller (CMAC),” Transactions of the Asme Journal of Dynamic Systems, vol. 97, no. 3, pp. 220-227, 1975.
C. C. Chung and C. M. Lin, “Fuzzy brain emotional cerebellar model articulation control system design for multi-input multi-output nonlinear,” Acta Polytechnica Hungarica, vol. 12, no. 4, pp. 39-58, 2015.
C. M. Lin and T. Y. Chen, “Self-organizing CMAC control for a class of MIMO uncertain nonlinear systems,” IEEE Transactions on Neural Networks, vol. 20, no. 9, pp. 1377, 2009.
S. A. Vorobyov, A. B. Gershman, and Z. Q. Luo, “Robust adaptive beamforming using worst-case performance optimization: a solution to the signal mismatch problem,” IEEE Trans. Signal Processing, vol. 51, pp. 313-323, Feb. 2003.
S. Shahbazpanahi, A. B. Gershman, and Z. Q. Luo, “Robust adaptive beamforming for general-rank signal models,” IEEE Trans. Signal Processing, vol. 51, pp. 2257-2269, Sept. 2003.
H. W. Yang, J. G. Huang, and C. Liu, “A modified diagonal loading adaptive beamforming method,” Computer Simulation, vol. 27, no. 3, pp. 318-321, 2010.
