Passive Radar Detection Approach for Multiple and Small Targets Based on Multicarrier and Sparse Reconstruction

In the detection scenario of passive radar (PR) for multiple and small targets, the signal-to-noise ratio (SNR) is low. Moreover, in the cross ambiguity function (CAF), the main-lobe and sidelobe widening of strong targets can obscure adjacent weak targets. To address the two challenges, this article proposes the following approaches. First, a synthesis algorithm is proposed for the CAF using the multicarrier signals to enhance the SNR. Second, a CAF calculation model is established based on compressive sensing (CS) and multicarrier signal to suppress main-lobe and sidelobe widening and detect weak targets. Finally, a sparse reconstruction algorithm based on ${l}_{0}$ pseudonorm and iterative solution is proposed to solve the difficulty of estimating input parameters. The experimental results show that the proposed approaches achieve an improvement in SNR and enable weak target detection. Compared to other sparse reconstruction algorithms and 2-D constant false alarm rate (CFAR) detection algorithms, the proposed approaches have a higher detection rate in the multiple and small targets’ detection.