Blind Beamforming Algorithm Based on Sparse Time-frequency Decomposition
Funds:
The National Natural Science Foundation of China (61401469)
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摘要: 針對(duì)現(xiàn)有盲波束形成算法通用性差，所需采樣數(shù)據(jù)量大等問題，該文提出一種基于稀疏時(shí)頻分解的盲波束形成算法。算法首先將傳統(tǒng)的短時(shí)傅里葉變換轉(zhuǎn)化為稀疏重構(gòu)問題，利用交替分裂Bregman算法進(jìn)行迭代求解。然后利用對(duì)各陣元的接收信號(hào)進(jìn)行稀疏時(shí)頻分解的結(jié)果，結(jié)合聚類和不確定集方法，實(shí)現(xiàn)導(dǎo)向矢量的最優(yōu)估計(jì)。最后利用MVDR算法獲得最優(yōu)權(quán)矢量。該算法無需利用信號(hào)統(tǒng)計(jì)特性，實(shí)現(xiàn)了高效的盲波束形成。仿真實(shí)驗(yàn)結(jié)果表明，該算法所需數(shù)據(jù)量小，迭代步驟易于工程實(shí)現(xiàn)，較現(xiàn)有盲波束形成算法輸出性能更優(yōu)，適用范圍更廣。
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關(guān)鍵詞:
- 盲波束形成 /
- 時(shí)頻分解 /
- 稀疏重構(gòu) /
- 導(dǎo)向矢量估計(jì)
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. -
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