Improving Range Ambiguity Suppression in Pulse-Doppler Radar Systems With Nonlinear Processing and Joint Reversible Mismatch Filtering

Pulse-Doppler (PD) radar systems are extensively utilized for detecting and tracking moving targets. However, conventional PD radar suffers from range ambiguity due to the regularities in waveform. Previous researches have attempted to resolve this issue using techniques such as multi-pulse repetition frequencies scheme, waveform diversity, nonlinear ambiguity suppression (NLAS) and sparse reconstruction. Despite their efforts, these techniques exhibit limitations, including increased search time, residual ambiguous energy, signal distortion, and model mismatch. To overcome these limitations, this paper proposes a novel nonlinear processing approach named “INLAS-JRMMF” for suppressing range ambiguity, which combines a joint reversible mismatched filter (JRMMF) with improved NLAS. In general, INLAS-JRMMF scheme achieves range ambiguity suppression by transforming the signals into the range-Doppler domain, removing the undesired ambiguous energy, and retrieving radar signals by inverse transformation. To successfully implement this scheme and improve the range ambiguity suppression performance, we have designed an iterative processing framework, and two reversible mismatched filters, i.e., RMMF and JRMMF. The proposed method can effectively address the problems of signal distortion, residual ambiguous energy, and range sidelobe modulation effect, as well as enjoys low integrated sidelobe level and signal-to-noise loss. The effectiveness of the proposed approach is evaluated through simulations and the processing of measured data. The results demonstrate that the INLAS-JRMMF method surpasses the NLAS method in terms of range ambiguity suppression and radar imaging outcomes.