Toward Control-Friendly Aerial Assistance: A Minimum-Movement UAV Online Trajectory Design for Mobile ILAC Service Coverage

Abstract

This paper addresses trajectory planning for an uncrewed aerial vehicle (UAV) providing integrated localization and communication (ILAC) services to a mobile ground user (GU) in infrastructure-denied environments. Existing studies predominantly optimize the communication and localization performance while neglecting UAV maneuverability constraints, resulting in erratic trajectories with frequent directional changes—the so-called “ping-pong” effect—that compromise flight stability and practical deployment. To overcome this limitation, we formulate a two-dimensional online trajectory optimization problem that minimizes the UAV’s flight distance while satisfying the constraints on communication rate and localization accuracy. Leveraging the $D$ -optimality criterion for evaluating the localization accuracy, we derive conditions for approximating the optimal determinant (opt- $D$ ) metric and analytically characterize the feasible flight region as two oppositely-oriented hyperbolic regions. Such characterization effectively converts the non-convex localization accuracy constraint into a convex region, thereby enabling the efficient design of an online trajectory optimization algorithm. To address the random user mobility issue, we apply a model predictive control (MPC) framework augmented with an extended Kalman filter (EKF)-based multi-step motion prediction. Numerical results demonstrate that the proposed approach reduces the average flight distance and the trajectory curvature root mean square by 70.6% and 59.1%, respectively, across all benchmarks. Meanwhile, it not only effectively suppresses the “ping-pong” effect, but also achieves near-optimal localization accuracy within 1.1% of the best-performing benchmark, and strictly satisfies the prescribed communication rate requirement.