Spatio-temporal characterization of airborne radio frequency environments

This paper presents a nonparametric model for learning the radio frequency signal propagation in airborne environments. Spatio-temporal Gaussian processes are used to capture variations from the empirical path loss model. The Gaussian process is constructed using signal strength measurements from a known emitter. We show how this model can be used for improving the localization of an unknown emitter in the same environment.