G2A Localization: Aerial Vehicles Localization Using a Ground Crowdsourced Network.

In this paper, we address the ground-to-air (G2A) localization problem using a crowd- sourced network with a mix of synchronized and unsynchronized receivers. First, we use a dynamic model to represent the offset and the skew of the unsynchronized receivers. This model is then used with a Kalman filter (KF) to compensate for the drifts of the unsynchronized receivers’ clocks. Subsequently, the location of the aerial vehicle (AV) is estimated using another KF with the multilateration (MLAT) method and the dynamic model of the AV. We demonstrate the performance advantages of our method through a dataset collected by the OpenSky network. Our results show that the proposed dual KF method decreases the average localization error by orders of magnitude compared with a solo multilateration method. In particular, the proposed method brings the average localization error from tens of kilometers down to hundreds of meters, based on the considered dataset.