发表机构
Michigan State University（密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对未知位姿中继的隐藏目标定位问题，提出轨迹诱导自校准方法，通过利用车辆轨迹和中继观测实现高精度目标定位，其性能优于基准方法且对异常值鲁棒。
AI 中文摘要
本文研究通过全局位置和偏航未知的中继信标所报告的测距测向数据包进行隐藏目标定位的问题。车辆知晓自身轨迹但从未直接感知目标；中继数据包仅包含相对于车辆局部坐标系的测距和测向信息，以及相对于隐藏目标的测距和测向信息。与仅测向网络定位、相对坐标系定位和目标包围控制不同，目标既未在车辆坐标系中被直接观测，也未被视为相对感知图中的节点。主要结果明确了消除由此产生的校准模糊性所需的最小运动：单个车辆位姿会留下连续的偏航/平移/目标尺度歧义，而在无噪声情况下，来自一个未知位姿中继的两个不同车辆相对观测可建设性地确定中继偏航（模2π）、中继位置和锚定目标。局部秩推论、共享目标多信标扩展以及轨迹分散性条件引理将中继自校准与有限窗口激励和原生测距测向估计联系起来。在蒙特卡洛评估中，该估计器以5.5毫米的均方根误差（RMSE）恢复隐藏目标，比每数据包30毫米的测距噪声低5倍，且比朴素扩展卡尔曼滤波（EKF）基准的精度高13倍；它能从2米的目标偏移和2.4弧度的偏航误差收敛到相同精度，而Huber加权在10%异常值损坏下保持毫米级精度，未受保护的估计器在此情况下成功率降至0.10。轨迹分散性可预测估计器质量：两个弱激励轨迹的条件数超过100，成功率分别为0.82和0.70，而每个充分激励的轨迹都达到完全成功。
英文摘要
This paper studies hidden-target localization from range-bearing packets reported by a relay beacon whose global position and yaw are unknown. The vehicle knows its own trajectory but never directly senses the target; the relay packet contains only local-frame range and bearing to the vehicle and to the hidden target. Unlike bearing-only network localization, relative-frame localization, and target-enclosing control, the target is neither directly observed in the vehicle frame nor treated as a node in a relative-sensing graph. The main result characterizes the minimal motion that removes the resulting calibration ambiguity: one vehicle pose leaves a continuous yaw/translation/target gauge, whereas two distinct vehicle-relative observations from one unknown-pose relay constructively determine relay yaw (modulo 2 pi), relay position, and the anchored target in the noiseless case. A local rank corollary, a shared-target multi-beacon extension, and a trajectory-spread conditioning lemma connect relay self-calibration to finite-window excitation and native range-bearing estimation. In Monte Carlo evaluation the estimator recovers the hidden target with 5.5 mm RMSE, five times below the 30 mm per-packet range noise and thirteen times more accurate than a naive EKF baseline; it converges to the same accuracy from 2 m target offsets and 2.4 rad yaw errors, and Huber weighting preserves millimeter accuracy under 10% outlier corruption that drops the unprotected estimator to a 0.10 success rate. Trajectory spread predicts estimator quality: the two weakly excited trajectories carry condition numbers above 100 with success rates of 0.82 and 0.70, while every well-excited trajectory attains full success.
Comments10 pages, 4 figures, 9 tables
