An Autofocus Algorithm for Estimating Residual Trajectory Deviations in Synthetic Aperture Radar
Due to the accuracy limitation of the navigation system, deviations between the real trajectory and the measured one appear inevitably in airborne synthetic aperture radar (SAR), which degrades the image quality dramatically. To improve the focusing performance, these trajectory deviations should be well estimated and compensated. In this paper, a data-based autofocus approach is proposed to correct the residual 3-D trajectory deviations. This new approach mainly contains two processing stages. The first stage is the local phase error estimation procedure involving small images autofocusing. A gradient function considering smoothness regularization is developed to efficiently achieve the sharpness-maximizing local phase error functions. In the second stage, the local phase error functions are combined to retrieve the residual 3-D trajectory deviations by a proposed weighted total least square method. This approach has been applied on highly squinted and large-swath airborne SAR raw data, respectively. Both real data experiments generate well-focused SAR images by the estimated trajectory parameters, and thus, validate the effectiveness of the proposed autofocus approach.