An NCS-Based WLS Estimator for Airborne Microwave Photonic SAR Autofocus

The motion error of airborne microwave photonic synthetic aperture radar (SAR) has 2-D spatial variation characteristics, and the range spatial variant motion error (RVE) and azimuth spatial variant motion error (AVE) significantly interplay during the motion error estimation. For the RVE estimation, the standard weighted least square (WLS) algorithms are susceptible to the AVE and the moving targets. In addition, the azimuth subimage-based WLS algorithms face the problem of dominant points decreasing dramatically. This letter proposes a WLS estimation kernel based on nonlinear chirp scaling (NCS) to address the issues above. The AVE is first significantly corrected by the NCS processing, and RVE is subsequently estimated using the standard WLS kernel. In addition to eliminating the adverse effects of AVE and moving targets, the proposed method can retain sufficient dominant points to ensure the accuracy of phase gradient autofocus (PGA). The measured data processing results verify the effectiveness of the proposed method.