Energy-Aware UAV-Enabled Target Tracking: Online Optimization with Location Constraints

TL;DR

This work tackles online UAV trajectory design for target tracking under practical propulsion-energy and fixed launch/landing constraints. It introduces a two-subproblem framework: a candidate trajectory that minimizes the weighted sum of predicted PCRBs $\alpha \breve{\mathrm{PCRB}}_{\text{x},n} + (1-\alpha) \breve{\mathrm{PCRB}}_{\text{v},n}$ to maximize sensing accuracy, and an energy-aware backup trajectory that guarantees feasibility within the energy budget $E_{\text{tot}}$. Efficient solutions are developed: the candidate problem (P2.n) is addressed via a Dinkelbach transform and the Lasserre hierarchy to yield a global optimum under a sufficient condition, while the backup problem (P3.n) is solved by successive convex approximation (SCA). Numerical results show that the proposed online approach achieves substantially lower PCRBs and more flexible energy utilization than benchmarks, while ensuring feasibility for the initial-final and energy constraints in real time. The method provides a practical framework for energy-aware, online UAV sensing and can be extended to cooperative multi-UAV scenarios.

Abstract

For unmanned aerial vehicle (UAV) trajectory design, the total propulsion energy consumption and initial-final location constraints are practical factors to consider. However, unlike traditional offline designs, these two constraints are non-trivial to concurrently satisfy in online UAV trajectory designs for real-time target tracking, due to the undetermined information. To address this issue, we propose a novel online UAV trajectory optimization approach for the weighted sum-predicted posterior Cramér-Rao bound (PCRB) minimization, which guarantees the feasibility of satisfying the two mentioned constraints. Specifically, our approach designs the UAV trajectory by solving two subproblems: the candidate trajectory optimization problem and the energy-aware backup trajectory optimization problem. Then, an efficient solution to the candidate trajectory optimization problem is proposed based on Dinkelbach's transform and the Lasserre hierarchy, which achieves the global optimal solution under a given sufficient condition. The energy-aware backup trajectory optimization problem is solved by the successive convex approximation method. Numerical results show that our proposed UAV trajectory optimization approach significantly outperforms the benchmark regarding sensing performance and energy utilization flexibility.

Figures (3)

• Figure 1: A UAV-enabled target tracking system with initial and final locations.
• Figure 2: The proposed approach procedures.
• Figure 3: Comparisons between the UAV trajectory, the consumed propulsion energy, and the sensing performance under different cases.