未知位姿测距测角中继的激励监督闭环自标定与目标寻优
Excitation-Supervised Closed-Loop Self-Calibration and Target Seeking for an Unknown-Pose Range-Bearing Relay
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中文总结 AI 辅助
针对未知位姿测距测角中继，本文提出激励监督闭环自标定与目标寻优算法，通过轨迹扩展裕度$S_v$监督控制器，经仿真与实验验证其标定精度及目标寻优成功率优于固定调度方法。
中文摘要 AI 辅助
车辆通过位置和偏航未知的测距测角中继寻找隐藏目标时，需在线判断自身运动是否已使中继标定可信，以及不可信时应采取何种行动。已有研究指出，两种不同的车辆相对观测可消除标定的不确定性，使中继本地数据包对目标全局可操作（arXiv:2608.09464），但该结论是静态的，仅事后对存储窗口进行分类。本文提供闭环层：研究表明，决定可辨识性的轨迹扩展裕度$S_v$同时是有限噪声种子精度界、局部向量方差分解和圆几何激励预算，据此设计激励重置控制器。激励监督算法在扩展证书不足时重新触发探索性运动，将目标寻优输入从激励的推动方向投影，否则进入无约束目标寻优阶段。在明确采样假设下，该监督规则可在有限时间内获取所需激励；在无噪声局部区域且激励衰减为正时，估计器收敛后目标寻优也收敛；阈值根据期望标定精度水平选择，而非启发式选取。闭环仿真、配对蒙特卡洛对比、扩展阈值消融实验，以及带传感延迟的ROS 2/Gazebo软件在环实验验证了该方法。衰减率扫描显示，当固定调度的衰减速度超过未知的充足激励所需时间时，监督机制至关重要：100次配对试验中，固定基线的偏航RMSE从0.010升至0.065 rad，成功率降至56%，而目标跟踪误差保持稳定；监督机制使偏航RMSE维持在0.0095至0.0191 rad之间，成功率达100%。
英文摘要
A vehicle seeking a hidden target through a range-bearing relay of unknown position and yaw must decide, online, whether its own motion has already made the relay calibration trustworthy, and what to do when it has not. Two distinct vehicle-relative observations are known to remove the calibration gauge and make the target's relay-local packet globally actionable (arXiv:2608.09464), but that statement is static: it classifies a stored window only after the fact. This paper supplies the closed-loop layer: we show that the trajectory-spread margin $S_v$ that governs identifiability is simultaneously a finite-noise seed-accuracy bound, a local-vector variance decomposition, and a circle-geometry excitation budget, and we use it to supervise an excitation-reset controller. An excitation-supervised algorithm retriggers exploratory motion whenever the spread certificate is insufficient, projecting the target-seeking input away from the excitation's push, and otherwise proceeds to unrestricted target seeking. Under explicit sampling assumptions the supervision rule provably acquires any required excitation in finite time; in the noiseless local regime with positive excitation decay, estimator convergence yields target-seeking convergence after certification; and the threshold is selected from a desired calibration-accuracy level rather than chosen heuristically. Closed-loop simulation, paired Monte Carlo comparisons, a spread-threshold ablation, and a ROS 2/Gazebo software-in-the-loop experiment with sensing delay validate the approach. A decay-rate sweep shows that supervision matters when a fixed schedule's decay outruns the unknown time-to-adequate-excitation: over 100 paired trials the fixed baseline's yaw RMSE rises from 0.010 to 0.065 rad and success falls to 56%, while target-tracking error remains insensitive; supervision keeps yaw RMSE between 0.0095 and 0.0191 rad with 100% success.
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。
