LAUSR

For azimuth multichannel synthetic aperture radar (SAR), unavoidable inconsistency errors between channels can degrade SAR image quality severely, leading to possible ghost targets, image defocusing, and so on. To address this issue, a novel channel inconsistency estimation method is proposed based on maximum normalized image sharpness (NIS). First, channel amplitude and time delay errors are corrected in the coarse compensation step. Then, images of each channel are attained by azimuth spectrum recovery and imaging processing. Next, range-variant channel phase errors are estimated via optimizing NIS, which reaches the maximum value when the image is focused well or ghost targets are suppressed completely. The Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is employed to get the optimal solution based on the derived gradient of the objective function. Finally, the ultimate image is formed by adding up phase-compensated images of each channel. By optimizing the focused image quality, the proposed algorithm achieves high estimation accuracy. Simulated data and real multichannel SAR data are processed to demonstrate the effectiveness of the proposed method.