An Imaging Algorithm for Multi-Channel SAR Azimuth Missing Data

In the process of synthetic aperture radar (SAR) data acquisition, aircraft are susceptible to various disturbances during flight, resulting in the loss or destruction of azimuth echo data, which leads to blurring of SAR images. To solve the problem of imaging blurring caused by missing data in azimuth, an imaging algorithm integrated with spectrum reconstruction based on adaptive beamforming is proposed. First, the multi-channel SAR azimuth missing data model is analyzed, and the multiple spatial freedoms provided by the multi-channel SAR system are applied to construct the spatial domain filter. Second, spatial adaptive beamforming is used for spatial filtering of each range-Doppler cell, and the spectrum of azimuth random missing data is reconstructed. Finally, the reconstructed spectrum data is imaged using the polar format algorithm (PFA). The point target simulation data and airborne multi-channel SAR real data are separately processed under the condition of both random and uniform missing data. Compared with the results of only PFA processing, the algorithm can eliminate false targets caused by periodic missing data and significantly improve the serious defocusing of point targets caused by random missing data. The entropy and the contrast of the real data image obtained by the proposed algorithm are improved by approximately 15% and 12%, respectively. The results verify the effectiveness of the algorithm.