Coordinated standoff tracking of in- and out-of-surveillance targets using constrained particle filter for UAVs

This paper presents a new standoff tracking framework of a moving ground target using UAVs with a limited sensing capability such as sensor field-of-view and motion constraints. To maintain persistent track of the target even in case of target loss (out of surveillance) for a certain period, this study predicts the target existence area using the particle filter, and produces control commands to ensure that all predicted particles can be covered by the field-of-view of the UAV sensor at all times. To improve target prediction/estimation accuracy, the road information is incorporated into the constrained particle filter where the road boundaries are modelled as nonlinear inequality constraints. Both Lyapunov vector field guidance and nonlinear model predictive control methods are applied for the standoff tracking and phase angle control, and the advantages and disadvantages of them are compared using numerical simulation results.