Resilient Deployment of Low-Altitude Collaborative Surveillance Networks: A Max–Min Trajectory Exposure Approach
Unmanned aerial vehicle (UAV) operations are creating diverse application scenarios with the development of sixth-generation (6G) mobile networks. Consequently, low-altitude airspace surveillance has become a critical issue for public security. However, existing surveillance systems primarily rely on single-site deployment or simple cooperative monitoring, which constrain the stability of collaborative sensing capabilities. This paper investigates the low-altitude collaborative surveillance network (LACSN) deployment problem from the perspective of resilience enhancement, targeting the efficient sensing of highly mobile UAVs under complex practical constraints. First, we introduce a novel surveillance evaluation metric, namely the Drone Exposure Index (DEI), to quantify the effectiveness of collaborative surveillance networks in urban low-altitude scenarios. Second, taking the DEI as the main optimization objective, the LACSN deployment problem is formulated as a max–min optimization model that jointly enhances spatial coverage and sensing robustness while satisfying communication constraints. Third, to address uncertainty in UAV flight trajectories, we develop a column-generation-based approach that integrates a model reformulation procedure with an adversarial oracle. Experiments on representative scenarios demonstrate the effectiveness of the proposed method in terms of sensing and coverage.