Interpolation of Position Estimates for Radio Fingerprinting using Gaussian Process Regression

Radio fingerprinting is a well-established concept for modeling the propagation of Wi-Fi, Bluetooth or other radio signals in a known environment for localization purposes. When using position estimates from such a model for trajectory tracking over time in a Bayesian framework such as a Particle Filter, it must be possible to evaluate arbitrary state hypotheses based on the fingerprinting model. Instead of having a single position estimate for current observations, the model target should be a continuous probability density function from which any hypothesis can be evaluated. This work proposes an interpolation approach for the position estimates of fingerprinting models. The interpolation is based on Gaussian Process Regression. The proposed approach is particularly capable of handling multimodalities and maintains high accuracy even in large environments with little calibration data. It is also efficient in terms of runtime, making it a suitable choice for real-time applications.