Multi-UAV Enabled Sensing: Cramer-Rao Bound Optimization

The unmanned aerial vehicles (UAV) is envisioned as a promising aerial facility for providing advanced communication services as well as sensing functionalities in next-generation wireless system. This paper considers a multi-UAV based integrated sensing and communications (ISAC) system, where multiple UAVs transmit ISAC signals to a unknown moving ground user (GU) for dynamic tracking in presence of an ISAC-base station (BS). In particular, we first harness the extended Kalman filtering (EKF) method for predicting and tracking the motion parameters of GU at each time slot, which relies on the range measurements extracted from the echoes at the ISAC-BS. Next, we formulate an optimization problem for multi-UAV real-time trajectories design that aims to optimize the derived Cramer-Rao bound (CRB). To address the resultant non-convex problem, we develop an efficient iterative algorithm to obtain a near optimal solution by utilizing the successive convex approximate (SCA) technique. Our numerical simulations illustrate that our proposed algorithm can track the GU accurately and achieve compelling sensing performance.