Compressed Sensing-Based Iterative Echo Reconstruction for Range Ambiguity Suppression in Pulse-Doppler Radar

Conventionalpulse-Doppler (PD) radar systems with medium or high pulse repetition frequency often suffer from severe range ambiguity caused by coherent pulse trains. To address this issue, waveform diversity has been proposed as a solution. However, it is ineffective in suppressing folded clutter because the near-range clutter echoes are stronger than long-range target echoes. Leveraging the isolation properties of waveforms, the compressed sensing (CS) algorithm is employed to reconstruct the corresponding echoes of diverse waveforms. However, the reconstruction accuracy is degraded by the ambiguity energy. In this article, we propose a CS-based iterative echo reconstruction method in PD radar systems that transmit phase-coded agile signals. The proposed method aims to improve the reconstruction accuracy of conventional CS methods and enhance range ambiguity suppression performance. First, the sparsity adaptive matching pursuit and estimation (SAMPE) algorithm is proposed to accurately approximate the sparse vector, enabling scatter information estimation and echo reconstruction. Then, to further optimize the accuracy of echo reconstruction, the SAMPE algorithm is integrated with the iterative scheme, known as the ISAMPE algorithm, which effectively reconstructs the echo of each range segment. Furthermore, the proposed method can directly process compressed samples, breaking the relationship between the radar signal bandwidth and the sampling rate. Several simulations and a ground-based experiment demonstrate the effectiveness of the proposed method in suppressing range ambiguity while improving the accuracy of echo reconstruction.