A Joint Multiple Hypothesis Tracking and Particle Filter Approach for Aerial Data Fusion

The use of Unmanned Aerial Vehicles (UAV) has increased in recent years. Increased density of air traffic as well as the autonomy of the vehicles involved, demand robust safety of traffic operations in terms of dependable decision making for flight operations. Since future traffic management services (U-space) will focus on registration, identification, approval to fly, etc., and cooperative traffic avoidance such as FLARM requires that other parties be equipped as well, future UAVs should be able to robustly detect uncooperative parties and avoid mid-air collisions in airspace. To ensure the highest robustness and to increase sensitivity and accuracy, a combination of several sensors systems by multi-sensor data fusion techniques is highly recommended. This paper formulates a novel multi sensor data fusion algorithm, that is a joint approach of Multiple Hypothesis Tracking algorithm and Particle Filtering. The union of these two algorithms combines the strength of the Multiple Hypothesis Tracking for data association with the robustness of the Particle Filter to estimate the position of the tracked objects. This joint approach has been validated with the use of simulated data.