Summary
In city environments, errors in GPS can cause location data to be shifted, making radio signal maps inaccurate even if many measurements are taken. The authors present a way to fix these location errors after data collection by using the radio signals themselves to adjust the positions. They combine the GPS errors with radio signal patterns in a mathematical model to better estimate where the signals were actually measured. Their method reduces errors significantly compared to methods that ignore position errors or only smooth trajectories. This shows it’s possible to improve maps and location data quality by using the signals as clues after measurements are taken.
What this means in practice
- •For wireless network engineers: Improve radio environment maps by calibrating location errors after deployment using the proposed joint estimation method.
- •For iot deployment teams: Enhance the accuracy of location-tagged signal data for IoT applications in urban areas where GPS errors affect positioning.
Abstract
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.