发表机构
James Watt School of Engineering, University of Glasgow（格拉斯哥大学詹姆斯·瓦特工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多运营商ISAC系统中无人机跟踪问题，提出双时间尺度联邦校准框架，通过共享校准参数与协方差交集融合实现可靠预测，显著降低RMSE并节省功率。
AI 中文摘要
在涉及多个运营商的集成感知与通信（ISAC）系统中，无人机（UAV）跟踪可以受益于多个基站的感知，而原始感知记录、信道信息和传输决策仍保留在各运营商本地。由此产生的问题是，如何从不同的跟踪估计中获得可靠的无人机预测，并利用该预测指导传输，而无需集中其数据或控制变量。为解决这一问题，我们提出了一种双时间尺度框架，包括慢时间尺度的运动不确定性校准和带有预测波束成形的在线轨迹融合。本地雷达创新校准每个跟踪器对未建模无人机运动所分配的不确定性，仅将所得参数在运营商之间共享。在在线跟踪期间，协方差交集（CI）结合本地估计，无需知道其估计误差之间的相关性。融合后的预测随后指导每个运营商的传输设计，需满足通信服务质量、功率和跟踪约束。我们进一步建立了通信协方差的精确秩一恢复以及本地跟踪不确定性的有限区间界限。仿真表明，在显著的模型失配下，跟踪不确定性的一致性得到改善。CI避免了相关误差下的过度自信，而使用当前融合预测相比仅针对通信设计的传输，均方根误差（RMSE）降低了约31%，额外功率不到2%。消融实验进一步表明，当前预测与过时预测之间的功率差异主要由预测协方差而非预测均值驱动。
英文摘要
In integrated sensing and communication (ISAC) systems involving multiple operators, unmanned aerial vehicle (UAV) tracking can benefit from sensing at multiple base stations, while raw sensing records, channel information, and transmit decisions remain local to each operator. The resulting problem is to obtain a reliable UAV prediction from different track estimates and use it to guide transmission without centralizing their data or control variables. To resolve this, we propose a two-timescale framework comprising slow-timescale motion-uncertainty calibration and online track fusion with predictive beamforming. Local radar innovations calibrate the uncertainty assigned by each tracker to unmodeled UAV motion, with only the resulting parameters shared across operators. During online tracking, covariance intersection (CI) combines local estimates without requiring knowledge of correlations between their estimation errors. The fused prediction then guides each operator's transmission design subject to communication QoS, power, and tracking constraints. We further establish exact rank one recovery for the communication covariances and finite interval bounds on local tracking uncertainty. Simulations show improved consistency of tracking uncertainty under substantial motion model mismatch. CI avoids overconfidence under correlated errors, while using the current fused prediction reduces RMSE by about 31% relative to transmission designed for communication alone, with less than 2% additional power. Ablation further shows that the power difference between current and outdated predictions is driven mainly by predictive covariance rather than predictive mean.
Comments13 pages, 6 figures
