发表机构
NVIDIA（英伟达）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对局部多径抑制破坏协同感知证据的问题，提出分阶段锚定恢复方法，利用未用强锚点查询弱报告，在1,024次五无人机实验中增加30个匹配目标且弱上传量减少97.4%，并证明无损残差筛选。
AI 中文摘要
局部多径抑制可能消除协同感知所需的证据。我们提出分阶段锚定恢复方法：先保留仅强确认的结果，再利用未使用的强锚点查询兼容的弱报告。对于任意数量的感知节点，我们证明了无损残差筛选，并推导出佐证定律和双向成本定律。在1,024次五无人机投放中，与严格共识相比，恢复方法增加了30个匹配目标并产生3个虚假输出，匹配单次锚定确认的检测数量，同时将弱上传量减少97.4%。等大小的提示/报告记录相比上传所有合格报告，负载减少4.5%。独立验证恢复了4个额外目标，且未观察到虚假输出。
英文摘要
Local multipath rejection can erase evidence needed for cooperative sensing. We propose staged anchored recovery: preserve strong-only confirmations, then query compatible weak reports using unused strong anchors. For any number of sensing nodes, we prove lossless residual screening and derive corroboration and bidirectional cost laws. In 1,024 five-UAV drops, recovery adds 30 matched targets and three false outputs over strict consensus, matching one-pass anchored confirmation's detection counts while reducing weak uploads by 97.4%. Equal-sized cue/report records yield 4.5% less payload than uploading all eligible reports. Independent validation recovers four additional targets with no observed false outputs.
Comments4 pages, 3 figures, 2 tables
