Active Target Tracking and Cooperative Localization for Teams of Aerial Vehicles

This paper studies the active target-tracking problem for a team of unmanned aerial vehicles equipped with 3-D range-finding sensors. We propose a gradient-based control strategy that encompasses the three major optimum experimental design criteria, and we use the Kalman filter for estimating the target's position both in a cooperative and in a noncooperative scenario. Our control strategy is active because it moves the vehicles along paths that minimize the uncertainty about the location of the target. In the case that the position of the vehicles is not perfectly known, we introduce a new and more challenging problem, termed active cooperative localization and multitarget tracking (ACLMT). In this problem, the aerial vehicles must reconfigure themselves in the 3-D space in order to maximize both the accuracy of their own position estimate and that of multiple moving targets. For ACLMT, we derive analytical lower and upper bounds on the targets' and vehicles' position uncertainty by exploiting the monotonicity property of the Riccati differential equation arising from the Kalman-Bucy filter. These bounds allow us to study the impact of the sensors' accuracy and the targets' dynamics on the performance of our coordination strategy. Extensive simulation experiments illustrate the proposed theoretical results.