一种基于贝叶斯估计的旋转轨道SAR建筑物三维成像方法研究OA

Synthetic aperture radar(SAR)plays a crucial role in the health monitoring of urban buildings.However,in two-dimensional imaging,it encounters challenges such as geometric distortion(overlap,shadowing,and foreshorten-ing)because of the complexity of building structures.As SAR uses the geometric deformations in two-dimensional pro-jections to obtain three-dimensional structural information,3D imaging becomes highly significant in building health monitor-ing.This paper proposes a Bayesian estimation-based rotational millimeter-wave SAR 3D imaging model,as a new approach for building 3D point cloud reconstruction and high-precision height calculation.To address the image quality degradation caused by sidelobes and speckle noise,a multiangle millimeter-wave radar system was developed for data acquisition through rotation along a linear trajectory.A hierarchical matching strategy based on height hypotheses was employed to solve the Range-Doppler(R-D)equation,using correlation matching to improve efficiency.To address point matching errors,a hy-brid distribution model based on stereo matching was developed.Geometric offsets were modeled as target errors,to mini-mize error distribution,optimize elevation,and accelerate 3D imaging.Experiments using a 77 GHz millimeter-wave radar for multiangle data acquisition and 3D point cloud imaging demonstrated an imaging accuracy of 0.412 m compared with Li-DAR point cloud heights,verifying the method's effectiveness and imaging mode reliability.