ISAR Imaging for Nonco-operative Targets Based on Sharpness Criterion Under Low SNR

Inverse synthetic aperture radar (ISAR) imaging for noncooperative targets with complex translational motion (TM) and 3-D rotational motion (RM) face the problem of spatial-variant (SV) and high-order phase modulation. The existing ISAR technique cannot compensate for the phase modulation completely, especially under low signal-to-noise ratio (SNR). In this work, an efficient ISAR imaging approach is proposed for non-cooperative targets under low SNR. First, the signal model for noncooperative targets with TM and 3-D RM are established, where the high-order 2-D SV phase error are deduced by utilizing a nonstationary image projection plane model. Second, to mitigate the influence of noise, an adaptive denoising filter is generated by exploring the similarity between the profiles of echoes. In addition, inspired by the characteristic that all scatterers share the same TM, the compensating factors are extracted from the prominent scatterers, which can absolutely avoid the accumulation of residual TM errors. Meanwhile, the signal coherence is fully utilized to compensate for the SV phase errors caused by the RM. Finally, both simulated and electromagnetic data experiments validate the effectiveness and robustness of the proposed method.