UAV Trajectory Optimization for Sensing Exploiting Target Location Distribution Map

TL;DR

This work tackles UAV trajectory optimization for sensing a ground target when the exact location is unknown but governed by a known distribution stored in a target location distribution map $\bm{P}$. By coupling an expected-SNR map $\bm{S}$ with the distribution map, the authors formulate a non-convex NP-hard problem and develop three polynomial-complexity suboptimal solutions built on a graph-based reformulation and Lagrangian relaxation, as well as enhancements via waypoint deviations and a TSP-based augmentation. The proposed framework combines a discretized grid representation, a constrained shortest-path perspective, and map-based performance metrics to maximize the total sensing probability while meeting a per-slot SNR constraint $\bar{\rho}$ and a flight-distance limit $\bar{D}$. Numerical results show substantial gains over benchmarks, illustrating the effectiveness and scalability of the approach for practical cellular-enabled UAV sensing missions. The methods offer a tractable pathway to robust UAV sensing in environments with uncertain target locations and emphasize the value of distribution-aware planning in UAV-assisted sensing tasks.

Abstract

In this paper, we study the trajectory optimization of a cellular-connected unmanned aerial vehicle (UAV) which aims to sense the location of a target while maintaining satisfactory communication quality with the ground base stations (GBSs). In contrast to most existing works which assumed the target's location is known, we focus on a more challenging scenario where the exact location of the target to be sensed is unknown and random, while its distribution is known a priori and stored in a novel target location distribution map. Based on this map, the probability for the UAV to successfully sense the target can be expressed as a function of the UAV's trajectory. We aim to optimize the UAV's trajectory between two pre-determined locations to maximize the overall sensing probability during its flight, subject to a GBS-UAV communication quality constraint at each time instant and a maximum mission completion time constraint. Despite the non-convexity and NP-hardness of this problem, we devise three high-quality suboptimal solutions tailored for it with polynomial complexity. Numerical results show that our proposed designs outperform various benchmark schemes.

Figures (2)

• Figure 1: Illustration of an expected SNR map $\bm{S}$ and a target location distribution map $\bm{P}$.
• Figure 2: Illustration and performance of trajectory designs.