200 / 2019-12-30 13:56:00

Compressive Sensing-based Adaptive Beamforming

IEEE SAM 2020: Full Regular Papers > Adaptive beamforming

In the digital beamforming, each antenna sensor usually corresponds to a front-end chain, which dramatically increases the hardware costs. In this paper, compressive sensing-based adaptive beamforming is proposed to decrease the hardware complexity while effectively suppressing the interfering signals. Compressive sensing is utilized to reduce the sampling channel number, and sparse reconstruction based on the convex optimization model is used to accurately recover the full-array data. With the recovered data, the weight vector can be obtained by the robust adaptive beamforming. Simulation results demonstrate that the number of front-end chains is greatly reduced and the overall performance of the proposed beamforming is close to the optimal value. Moreover, the main-lobe width and sidelobe levels can be effectively cut down by improving the degrees-of-freedom of the beamforming.