On active target tracking and cooperative localization for multiple aerial vehicles

This paper presents a new cooperative active target-tracking strategy for a team of double-integrator aerial vehicles equipped with 3-D range-finding sensors. Our strategy is active because it moves the vehicles along paths that minimize the combined uncertainty about the target's position. We propose a gradient-based control approach that encompasses the three major optimum experimental-design criteria and relies on the Kalman filter for estimation fusion. We derive analytical lower and upper bounds on the target's 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 sensors' accuracy and target's dynamics on the steady-state performance of our coordination algorithm. Finally, in the case that the position of the vehicles is not perfectly known, we introduce a more challenging problem, termed Active Cooperative Localization and Multi-target Tracking (ACLMT). In this problem, the vehicles move in the 3-D space in order to maximize the accuracy of their own position estimate and that of multiple moving targets.