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 mainlobe and sidelobe widening of strong targets can obscure adjacent weak targets. To address the two challenges, this paper proposes the following approaches. First, a synthesis algorithm is proposed for the CAF using the multi-carrier signals to enhance the SNR. Second, a CAF calculation model is established based on compressive sensing (CS) and multi-carrier signal to suppress mainlobe and sidelobe widening and detect weak targets. Finally, a sparse reconstruction algorithm based on l₀ pseudonorm and iterative solution is proposed to solve the difficulty of estimating input parameters. Experimental results show that the proposed approaches achieve an improvement in SNR and enable weak target detection. Compared to other sparse reconstruction algorithms and two-dimensional constant false alarm rate (CFAR) detection algorithms, the proposed approaches have a higher detection rate in the multiple and small targets detection.

In the passive bistatic radar (PBR) system, methods exist to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delay-Doppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model partitions the CAF into blocks, identifies blocks requiring processing based on the presence of targets, and improves the construction and utilization of the measurement matrix. This results in a reduction in the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results indicate that the proposed approach increases the number of blocks by a factor of four, and reduces the number of real multiplications by approximately an order of magnitude. Hence, compared with the traditional approach, the proposed approach enables fast and stable detection of weak targets.