A Modified High-Precision Imaging Algorithm Based on Imaging Plane Optimization with Minimum Entropy Applied to UAV SAR

The miniaturized and lightweight unmanned aerial vehicle synthetic aperture radar (UAV SAR) is gradually becoming a research hotspot. The motion errors of UAVs lead to a deviation from a straight flight path couple with an unknown elevation of the target area, which leads to a degradation in SAR image quality. To achieve high-precision SAR imaging results, external terrain elevation information should be utilized. However, such data are challenging to obtain and limited in accuracy. In response to this problem, a modified high-precision imaging algorithm based on imaging plane optimization with minimum entropy is proposed. The proposed algorithm makes good use of the nonlinear trajectory of the UAV, which is unfriendly to imaging. Then, the image entropy is taken into account as the evaluation metric to acquire an approximated optimization imaging plane. Finally, the BP imaging is performed on the optimization imaging plane. The proposed method does not rely on external terrain information. Instead, it makes full use of the non-linear trajectory of the UAV and autonomously generates the optimal imaging plane for different terrain areas. By doing so, it achieves high-precision imaging results. Simulations and actual measurements have validated the effectiveness and enhancement of the proposed method.