用少量校准数据同时优化模型并校正误差，提升无线定位可靠性
Reliable Wireless Indoor Localization via Cross-Validated Prediction-Powered Calibration
- 联合优化预测模型与合成标签偏差估计
- 仅需少量数据即实现严格覆盖保证的定位结果
- 适合数据稀缺的室内无线定位场景
基于接收信号强度（RSSI）的无线室内定位依赖预测模型进行可靠的位置估计，但需恰当校准。现有方法常采用另一预测模型生成合成标签，但微调额外预测器及估计合成标签残差需额外数据，加剧了无线环境中的校准数据稀缺问题。本文提出一种高效利用有限校准数据的方法，可同步微调预测器并估计合成标签偏差，从而生成具有严格覆盖率保证的预测集。在指纹数据库上的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Wireless indoor localization using predictive models with received signal strength information (RSSI) requires proper calibration for reliable position estimates. One remedy is to employ synthetic labels produced by a (generally different) predictive model. But fine-tuning an additional predictor, as well as estimating residual bias of the synthetic labels, demands additional data, aggravating calibration data scarcity in wireless environments. This letter proposes an approach that efficiently uses limited calibration data to simultaneously fine-tune a predictor and estimate the bias of synthetic labels, yielding prediction sets with rigorous coverage guarantees. Experiments on a fingerprinting dataset validate the effectiveness of the proposed method.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。