Geolocation of Mobile Objects from Multiple UAV Optical Sensor Platforms

Abstract

With the rise of inexpensive, commercially available UAVs (drones) it has become possible to collect data from multiple UAVs equipped with optical sensors. This possibility has enabled tracking and data fusion with multiple airborne platforms. The addition of multiple airborne sensors allows for more robust tracking that is less susceptible to clutter and track proliferation. This paper demonstrates the air-to-ground tracking capabilities of two airborne sensors following a moving ground target using the centralized fusion Extended Kalman Filter and Probabilistic Data Association Filter implemented in the Python library pystemlib. The result of adding multiple airborne sensors is a reduced state estimation error and more robust target state predictions evidenced by a reduced root-mean-square error and smaller area of probabilities. A validation of this approach is demonstrated with real data.