Trajectory Planning for Joint Communications and Tracking in UAV-UGV Enabled Air-Ground Collaborative System

In this paper, we propose a novel uncrewed aerial vehicle (UAV) and uncrewed ground vehicle (UGV) enabled air-ground collaborative system (UAV-UGV-AGCS) where a UAV and a UGV are utilized to realize multi-functional integration. Specifically, the UAV simultaneously provides communication, data collection services, and tracks a ground mobile target (GMT); while the UGV assists in collecting data from clusters of sensor nodes (SNs) and receiving sensing echo signals, thereby achieving high-rate communication, efficient data collection, and real-time tracking. Based on the dynamic characteristics and collaboration of the UAV and UGV, a joint trajectory optimization problem is formulated to maximize the average system throughput, subject to the UAV and UGV motion constraints as well as energy consumption limits. In particular, we derive the posterior Cram & eacute;r-Rao bound (PCRB) as a tracking performance metric. To tackle the non-convexity problem, we first design the tracking method based on the extended Kalman filtering (EKF) to estimate the motion parameters of GMT in each time slot and group the SNs via the immune optimization (IO)-based clustering algorithm. Subsequently, the problem is decomposed into two subproblems and solved via a successive convex approximation (SCA)-based iterative optimization algorithm to obtain the optimal solutions. Simulation results demonstrate that the proposed framework achieves efficient data collection and superior communication performance, thereby meeting the demands of high-precision tracking by dynamically adjusting trajectories of UGV and UAV, compared with other benchmark network designs.