准静态定位误差下的事后位置校准无线电地图构建：联合估计、性能界与基于GNSS的评估
Radio Map Construction with Post-Hoc Location Calibration under Quasi-Static Positioning Errors: Joint Estimation, Performance Bounds, and GNSS-Based Evaluation
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中文总结 AI 辅助
本文提出利用RSS测量本身校准GNSS准静态偏移的无线电地图构建框架，联合估计偏移与传播参数，显著降低MSE差距，优于轨迹平滑基线。
中文摘要 AI 辅助
无线电地图支持环境感知的无线和物联网应用，可从移动设备收集的带位置标签的接收信号强度（RSS）测量中构建。在城市环境中，时间相关的GNSS误差可能使整个感知轨迹发生偏移，导致系统性的空间错位，而这种错位无法通过收集更多测量来缓解。本文提出了一种无线电地图构建框架，利用无线电测量本身在数据收集后校准错误的位置标签。主要定位误差被建模为传感器特定的准静态偏移，在高斯过程回归（GPR）框架中，通过利用距离相关路径损耗和空间相关阴影效应的互补空间信息，与无线电传播参数联合估计。我们建立了条件贝叶斯风险的下界和上界，并表明在平移不变轨迹模型下，仅轨迹信息无法识别准静态偏移，从而促使使用RSS衍生的空间信息进行校准。跨传播条件的数值评估表明，所提方法将理想GPR的均方误差（MSE）差距降低至约$3.26\mathrm{dB}^2$，而位置误差不可知和噪声输入GPR基线的差距约为$10\mathrm{dB}^2$。使用从智能手机GNSS测量导出的定位误差模型进行评估表明，尽管存在未建模的时变定位误差，所提方法仍优于KF--RTS轨迹平滑基线，在中位MSE下保持在理想GPR的约$5\mathrm{dB}^2$以内。这些结果表明，RSS测量不仅可作为无线电地图重建的观测数据，还可作为事后校准不完美地理标记感知数据的空间线索。
英文摘要
Radio maps enable environment-aware wireless and Internet-of-Things applications and can be constructed from location-tagged received signal strength (RSS) measurements collected by mobile devices. In urban environments, temporally correlated GNSS errors can shift an entire sensing trajectory, causing systematic spatial misregistration that is not mitigated by collecting more measurements. This paper presents a radio-map construction framework that uses the radio measurements themselves to calibrate erroneous location tags after data collection. The dominant positioning error is modeled as a sensor-specific quasi-static offset, which is jointly estimated with radio-propagation parameters in a Gaussian process regression (GPR) framework by exploiting complementary spatial information from distance-dependent path loss and spatially correlated shadowing. We establish lower and upper bounds on the conditional Bayes risk and show that, under a translation-invariant trajectory model, trajectory information alone cannot identify the quasi-static offset, thereby motivating the use of RSS-derived spatial information for calibration. Numerical evaluations across propagation conditions show that the proposed method reduces the mean squared error (MSE) gap from ideal GPR to approximately $3.26\mathrm{dB}^2$, compared with about $10\mathrm{dB}^2$ for position-error-agnostic and noisy-input GPR baselines. Evaluation using positioning-error models derived from smartphone GNSS measurements shows that the proposed method outperforms a KF--RTS trajectory-smoothing baseline despite unmodeled time-varying positioning errors, remaining within approximately $5\mathrm{dB}^2$ of ideal GPR at the median MSE. These results demonstrate that RSS measurements can serve not only as observations for radio-map reconstruction but also as spatial cues for post-hoc calibration of imperfectly geotagged sensing data.
发表机构
- The University of Electro-Communications（电子通信大学）
- Hokkaido University（北海道大学）
机构由 AI 辅助整理，请以论文原文为准。
