Reliable Wireless Indoor Localization via Cross-Validated Prediction-Powered Calibration

TL;DR

This paper introduces a calibration method for wireless indoor localization that uses limited data to fine-tune models and estimate bias, ensuring reliable position predictions with coverage guarantees.

Contribution

It presents a novel approach to calibrate predictive models with synthetic labels using minimal data, improving localization accuracy and reliability.

Findings

Effective calibration with limited data demonstrated on a fingerprinting dataset.

Prediction sets achieved rigorous coverage guarantees.

Method improves reliability of indoor localization models.

Abstract

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.