Exploiting antenna arrays for position tracking of unmanned aerial vehicles

This paper firstly identifies and discuses the main challenges in exploiting antenna arrays to track the position of unmanned aerial vehicles (UAVs). Then, a specific radio localization method based on the multiple differential phase-of-arrival (D-PoA) and time-of-arrival (TOA) measures at a 3-axial uniform linear array (3A-ULA) is presented to estimate the positron of an UAV with respect to a reference point. The D-PoA and TOA measures are coupled with a dynamic motion model of the UAV to enable the usage of non-linear Bayesian estimation methods such as the particle filter (PF) and the cuabture Kalman filter (CKF) to improve the positioning accuracy. Furthermore, a comparison in terms of accuracy and complexity of the PF and the CKF for the considered application is presented. To assess the estimation accuracy of these methods, a confined area random aerial trajectory emulator algorithm is used to generate actual paths of the flying UAVs.