Triangle Attribute-Guided Registration for Robust Multi-Target Association in Multi-UAV Airborne Radar Sensing

In multi-UAV airborne radar sensing, target-level fusion cannot start until detections reported in different UAV coordinate frames have been associated. This paper focuses on a single-epoch case: two UAV-mounted radars observe a common target group, but the reported point sets are sparse, unordered, only partially overlapping, and corrupted by angular noise, missed detections, and additional observations. In this setting, appearance cues, long tracks, and dense point-cloud neighborhoods are absent, so the association problem is mainly a sparse geometric registration problem. We propose a Triangle Attribute-Guided Robust Association (TARA) framework. TARA describes each target triple by its absolute scale and two normalized side-length ratios. These attributes are not used as final identity labels. Instead, they retrieve, verify, and rank plausible cross-view triangle pairs, from which a fixed number of rigid-transformation hypotheses are estimated. The best coarse transformation is then refined by the trimmed ICP, and the final correspondences are obtained by gated one-to-one matching in the aligned frame. The ablation results show why the attribute-guided step matters. With only 100 evaluated hypotheses, TARA reaches 93.7% F1 in the medium setting, while blind RANSAC needs about 10,000 random hypotheses to reach comparable accuracy. In composite degradation tests, TARA obtains mean F1-scores of 98.7%, 94.2%, 69.3%, and 69.1% from low to extreme conditions. The advantage is clearest when clutter, missing detections, and angular uncertainty appear together because the triangle attributes make the limited hypothesis budget much less likely to be spent on geometrically implausible triples.