Robust Beamforming Algorithm Based on Double-layer Estimation of Steering Vector and Covariance Matrix Reconstruction

LÜ Yan; CAO Fei; YANG Jian; FENG Xiaowei

doi:10.11999/JEIT211120

Volume 44 Issue 12

Dec. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(12): 4159-4167

LÜ Yan, CAO Fei, YANG Jian, FENG Xiaowei. Robust Beamforming Algorithm Based on Double-layer Estimation of Steering Vector and Covariance Matrix Reconstruction[J]. Journal of Electronics & Information Technology, 2022, 44(12): 4159-4167. doi: 10.11999/JEIT211120

Citation:

LÜ Yan, CAO Fei, YANG Jian, FENG Xiaowei. Robust Beamforming Algorithm Based on Double-layer Estimation of Steering Vector and Covariance Matrix Reconstruction[J]. Journal of Electronics & Information Technology, 2022, 44(12): 4159-4167. doi: 10.11999/JEIT211120

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Robust Beamforming Algorithm Based on Double-layer Estimation of Steering Vector and Covariance Matrix Reconstruction

doi: 10.11999/JEIT211120

LÜ Yan^{1, 2
,
,},
CAO Fei¹,
YANG Jian³,
FENG Xiaowei¹

1.
Nuclear Engineering College, Rocket Force University of Engineering, Xi’an 710025, China
2.
Unit 96746 of PLA, Korla 841000, China
3.
Missile Engineering College, Rocket Force University of Engineering, Xi’an 710025, China

Funds: The National Natural Science Foundation of China (62071481), The National Science Foundation for Young Scientists of China (61903375, 61501471)

Received Date: 2021-10-13
Accepted Date: 2021-12-27
Rev Recd Date: 2021-12-18

Available Online: 2022-01-13

Publish Date: 2022-12-16

Abstract

Abstract

Considering the problem of the low resolution of the Capon Power Spectrum (CPS) in the reconstruction of Interference plus Noise Covariance Matrix (INCM), two Robust Adaptive Beamforming (RAB) algorithms are proposed. The proposed algorithm first searches the peaks of CPS to determine the integration intervals and then eigen-decomposes the covariance matrixes obtained from the integration of each interval. The number of incident sources in the interval is determined by reasonably setting the decision threshold, and the eigenvectors corresponding to the larger eigenvalues are used as the preliminary estimation of the Steering Vectors (SV). Then, by maximizing the estimated power, the gap between the nominal SV and the real SV is searched in the orthogonal space of the nominal SV. The first proposed algorithm uses the eigenvector corresponding to the minimum eigenvalue to add the orthogonal proportional gradient to the initial estimated SV to obtain the double-layer estimated SV. The second proposed algorithm obtains the modified SV by solving a Quadratic Programming (QP) problem. Finally, the optimal weight vector of the array is obtained by reconstructing the INCM. Simulation results demonstrate that the proposed algorithm solves effectively the problem of the low resolution of the CPS estimation and is superior to other algorithms.
- Robust Adaptive Beamforming(RAB),
- Covariance matrix reconstruction,
- Steering Vector(SV) estimation,
- Capon Power Spectrum(CPS) estimation

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References(21)

References

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