Robust Online Trajectory Prediction for Non-cooperative Small Unmanned Aerial Vehicles

In recent years, unmanned aerial vehicles (UAVs) have got a boost in their applications in civilian areas like aerial photography, agriculture, communication, etc. An increasing research effort is being exerted to develop sophisticated trajectory prediction methods for UAVs for collision detection and trajectory planning. The existing techniques suffer from problems such as inadequate uncertainty quantification of predicted trajectories. This work adopts particle filters together with Löwner-John ellipsoid to approximate the highest posterior density region for trajectory prediction and uncertainty quantification. The particle filter is tuned and tested on real-world and simulated data sets and compared with the Kalman filter. A parallel computing approach for particle filter is further proposed. This parallel implementation makes the particle filter faster and more suitable for real-time online applications.