Low-Altitude UAV Tracking via Sensing-Assisted Predictive Beamforming

Sensing-assisted predictive beamforming, as one of the enabling technologies for emerging integrated sensing and communication (ISAC) paradigm, shows significant promise for enhancing various future unmanned aerial vehicle (UAV) applications. However, current works predominately emphasized on spectral efficiency enhancement, while the impact of such beamforming techniques on the communication reliability was largely unexplored and challenging to characterize. To fill this research gap and tackle this issue, this paper investigates outage capacity maximization for UAV tracking under the sensing-assisted predictive beamforming scheme. Specifically, a cellular-connected UAV tracking scheme is proposed leveraging extended Kalman filtering (EKF), where the predicted UAV trajectory, sensing duration ratio, and target constant received signal-to-noise ratio (SNR) are jointly optimized to maximize the outage capacity at each time slot. To address the implicit nature of the objective function, closed-form approximations of the outage probabilities (OPs) at both prediction and measurement stages of each time slot are proposed based on second-order Taylor expansions, providing an efficient and full characterization of outage capacity. Subsequently, an efficient algorithm is proposed based on a combination of bisection search and successive convex approximation (SCA) to address the non-convex optimization problem with guaranteed convergence. To further reduce computational complexity, a second efficient algorithm is developed based on alternating optimization (AO). Simulation results validate the accuracy of the derived OP approximations, the effectiveness of the proposed algorithms, and the significant outage capacity enhancement over various benchmarks, while also indicating a trade-off between decreasing path loss and enjoying wide beam coverage for outage capacity maximization.

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

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 Cram\'er-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.