结合压缩感知模型的稀疏阵列波束形成方法

Sparse array beamforming method combined with compressed sensing model

  • 摘要: 本文从稀疏阵列入手,将稀疏阵列接收数据模型转化为更高自由度下的单快拍接收数据模型,并将压缩感知模型引入稀疏阵列信号处理问题中,从理论上证明了其可行性。在等效单快拍数据下,利用稀疏重构算法准确估计信源方位和功率,进而对传统MVDR波束形成器进行优化。仿真结果表明,采用压缩感知模型实现稀疏阵列的波束形成,能够将稀疏阵列和压缩感知算法两者的优势结合,在阵列阵元数较少的条件下达到更高的自由度,同时具备良好的波束形成器性能。

     

    Abstract: Starting from the sparse array, this paper transformed the sparse array receiving data model into a single-shot data model with higher degrees of freedom, and introduced the compressed sensing model into the sparse array signal processing problem, which proves its feasibility in theory. Under the equivalent single snapshot data, the sparse reconstruction algorithm was used to accurately estimate the source orientation and power, and then the traditional MVDR beamformer is optimized. The simulation results show that the compressed sensing model can achieve the beamforming of sparse arrays, which can combine the advantages of both sparse array and compressed sensing algorithms, achieve higher degrees of freedom under the condition of fewer array elements, and have good Beamformer performance.

     

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