ISAC-Enabled Multi-UAV Collaborative Target Sensing for Low-Altitude Economy

TL;DR

This paper introduces a multi-UAV collaborative sensing scheme utilizing ISAC signals to improve dynamic target tracking in low-altitude environments, optimizing UAV trajectories, resource allocation, and UAV-BS association.

Contribution

It proposes a novel ISAC-enabled multi-UAV scheme with a low-complexity algorithm for joint optimization of UAV trajectories, resource allocation, and UAV-BS association for dynamic target sensing.

Findings

The proposed scheme outperforms benchmarks in sensing accuracy.

The iterative algorithm efficiently finds near-optimal solutions.

Numerical results validate the effectiveness of the joint optimization approach.

Abstract

Integrated sensing and communication (ISAC) has attracted growing research interests to facilitate the large-scale development of the low-altitude economy (LAE). However, the high dynamics of low-altitude targets may overwhelm fixed ISAC systems, particularly at the edge of their coverage or in blind zones. Driven by high flexibility, unmanned aerial vehicle (UAV)-assisted ISAC can provide more freedom of design to enhance communication and sensing abilities. In this paper, we propose an ISAC-enabled multi-UAV dynamic collaborative target sensing scheme, where UAVs can dynamically adjust their flight and resource allocation for cooperative sensing of mobile target through communicating with the terrestrial cellular network with ISAC signals. To achieve the precise sensing of the dynamic target, the posterior Cramer-Rao bound (PCRB) for the target state is derived. Subsequently, the PCRB minimization problem is formulated by jointly optimizing the UAV-BS association, UAVs' trajectories and bandwidth allocation, subject to the communication requirements for the UAVs. However, the problem is challenging since it involves non-convex and implicit objective function with coupled optimization variables. For a fast implementation of sensing and tracking, we propose a low-complexity iterative algorithm that can efficiently obtain a sub-optimal solution to the problem. Specifically, the UAV-BS association is first determined by the communication-optimal solution. Then the UAVs' trajectories and bandwidth allocation are alternatively optimized based on the descent direction search algorithm. Finally, numerical results are provided to validate the superiority of our proposed designs as compared to various benchmarks.