A non-parametric modeling of Time-of-flight ranging error for indoor network localization

For indoor network positioning, the ranging error modeling plays an important role in positioning algorithm optimization, simulation setup, system parameters calibration, performance evaluation and test-bed configuration, etc. However, the error model is commonly assumed as a Normal distribution or other distributions which cannot capture the negative value, the positive bias and the right-side tail phenomenon of the indoor ranging error. We use a non-parametric modeling for the ranging error, as the indoor wireless propagation is non-analytical. Seven error models are configured by distribution fitting with measurements from both stationary and mobile Time-of-flight (TOA) experiments implemented in typical indoor scenarios. Then the configured models are evaluated by Kolmogorov-Smirnov (KS) goodness-of-fit hypothesis test, indicating that a biased statistical model is sufficient to characterize indoor ranging and has a good fitting to real-world measurements. Further, the KS test results are verified by comparing the simulated and experimental positioning results. Our modeling method works for most indoor scenarios, and the modeling results can significantly improve the effective of simulations to reality.