基于图像匹配的高空大斜视无源目标定位OA

The workflow begins with approximate orthorectification of aerial images using coarse position-ing results and flight parameters,followed by extraction of the corresponding satellite image regions.RepVGG is employed to extract coarse image features,and initial correspondences are established via nearest-neighbor matching.Candidate pairs are then filtered using MiHo clustering and normalized cross-correlation(NCC).The refined correspondences are further enhanced by a Transformer module to achieve precise alignment.Based on the resulting high-precision matches,an angular error correction ma-trix is constructed,and iterative refinement is applied to effectively compensate for systematic errors.Ex-perimental results indicate that the proposed method substantially improves positioning accuracy relative to conventional approaches,achieving an approximately 70%enhancement in representative scenarios;even at a slant range of 90 km,the positioning error remains approximately 120 m.By leveraging image match-ing under challenging viewing geometries,the proposed passive localization method provides a novel solu-tion to limitations of conventional algorithms.A two-step matching strategy integrating Transformer-based feature enhancement with MiHo clustering-based filtering is introduced,effectively mitigating the ac-curacy degradation of traditional passive localization under high-altitude,large-oblique viewing conditions in the presence of small angular errors.