Improved Double Constraint Robust Capon Beamforming Algorithm

LI Lixin; BAI Tongtong; ZHANG Huisheng; BAO Tao; SHEN Libin

doi:10.11999/JEIT151213

Volume 38 Issue 8

Sep. 2016

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LI Lixin, BAI Tongtong, ZHANG Huisheng, BAO Tao, SHEN Libin. Improved Double Constraint Robust Capon Beamforming Algorithm[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2014-2019. doi: 10.11999/JEIT151213

Citation:

LI Lixin, BAI Tongtong, ZHANG Huisheng, BAO Tao, SHEN Libin. Improved Double Constraint Robust Capon Beamforming Algorithm[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2014-2019. doi: 10.11999/JEIT151213

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Improved Double Constraint Robust Capon Beamforming Algorithm

doi: 10.11999/JEIT151213

Funds:

The Fundamental Research Funds for the Central Universities (3102014JCQ01052, 3102014KYJD034, 3102014KYJD040), The Natural Science Foundation Research Project of Shaanxi Province (2016JM6062), The Aerospace Science and Technology Innovation Fund of CASC

Received Date: 2015-11-03
Rev Recd Date: 2016-03-31
Publish Date: 2016-08-19

Abstract

Abstract

Traditional double constraint robust Capon beamforming algorithm uses Newton iterative method for solving the optimal loading, presenting the problems of low accuracy and large amount of computation. An improved Double Constraint Robust Capon Beamforming (DCRCB) algorithm is proposed in this paper. The algorithm reconstructs the signal convariance matrix, and by optimizing the projection of signal steering vector onto the noise subspace, it projects the reconstructed interference-plus-noise convariance matrix onto the noise subspace, obtaining the double constraint algorithm model based on the noise subspace. For the norm constraint is accessorial, the algorithm model can be converted into a single constraint issue and be solved into an analytical expression of optimal diagonal loading finally. Simulation results show that the improved algorithm can optimize the side lobe by adjusting the beam width of the main lobe, improve effectively the anti-vector error robustness, and reduce the amount of computation.
- Adaptive beamforming,
- Double constraint Capon beamforming,
- Signal convariance matrix reconstrcting,
- Noise subspace

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References(29)

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