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.