Blind Beamforming Algorithm Based on Sparse Time-frequency Decomposition

CHEN Pei; ZHAO Yongjun; LIU Chengcheng

doi:10.11999/JEIT160995

Volume 38 Issue 12

Jan. 2017

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CHEN Pei, ZHAO Yongjun, LIU Chengcheng. Blind Beamforming Algorithm Based on Sparse Time-frequency Decomposition[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3078-3084. doi: 10.11999/JEIT160995

Citation:

CHEN Pei, ZHAO Yongjun, LIU Chengcheng. Blind Beamforming Algorithm Based on Sparse Time-frequency Decomposition[J]. Journal of Electronics & Information Technology, 2016, 38(12): 3078-3084. doi: 10.11999/JEIT160995

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Blind Beamforming Algorithm Based on Sparse Time-frequency Decomposition

doi: 10.11999/JEIT160995

Funds:

The National Natural Science Foundation of China (61401469)

Received Date: 2016-09-29
Rev Recd Date: 2016-12-05
Publish Date: 2016-12-19

Abstract

Abstract

A novel blind beamforming algorithm based on sparse Time-Frequency Decomposition (TFD) is proposed to solve the problems of existing blind beamforming algorithms: poor universality and the requirement of large amount of sampling data. In the proposed algorithm, the traditional Short-Time Fourier Transform (STFT) is first formulated as a sparse reconstruction problem. Then, a fast and efficient algorithm based on the alternating split Bregman technique is utilized to carry out the optimization. By combining the clustering and uncertainty set methods, the sparse-TFD results of the receiving data at each sensor are used to realize the estimation of Steering Vectors (SV). Finally, the optimal weight coefficients are achieved by substituting the estimated SV into the MVDR beamformer. The proposed algorithm hardly needs any specific statistical property of the receiving signals. Simulation results demonstrate that this algorithm can achieve superior output performance over the existing blind beamforming methods. It needs few snapshots with lower computational cost and has fast convergence rate, which makes the algorithm easy to utilize in practical applications.
- Blind beamforming,
- Time-frequency decomposition,
- Sparse reconstruction,
- Steering vector estimation

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References(31)

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