Focusing highly squinted missile-borne SAR data using azimuth frequency nonlinear chirp scaling algorithm

With the development of SAR imaging, the efficiency and rapidity of imaging algorithms have always been a research direction, and real-time imaging is a reflection of the speed of the imaging algorithm. For missile-borne SAR, real-time imaging is particularly important because it provides radar with a sense of the battlefield environment. In the case with a highly squinted angle, the azimuth space variance and the coupling between range and azimuth dimension will become serious in imaging, thus an azimuth frequency nonlinear chirp scaling (AFNCS) algorithm is proposed to solve this problem. Based on linear range walk correction, a novel chirp scaling algorithm is adopted to correct the range migration, and then a high-order phase filtering factor is introduced into the azimuth dimension frequency domain to decrease the azimuth space variance. In addition, combined with the SPECtral Analysis method, the image is focused on the Doppler domain. The AFNCS algorithm does not need complex mathematical calculations such as interpolation and can meet the real-time requirement of missile-borne SAR imaging. Simulation results illustrate the effectiveness of the proposed algorithm. The integration of the research in this paper and the deep learning will further pave the way for real-time SAR imaging applications in disaster monitoring, security and surveillance.