Scalable online trajectory and resource optimization for low-altitude ISAC

Develop scalable online trajectory and resource optimization methods for low-altitude integrated sensing and communications under imperfect channel information, uncertain target motion, limited onboard energy, residual interference, and clutter.

Background

Low-altitude ISAC can treat aircraft trajectories as controllable spatial resources and jointly optimize them with beamforming, power allocation, scheduling, and other communication–sensing variables. Existing approaches commonly assume finite mission periods, deterministic line-of-sight channels, and known or estimated user and target states.

As the numbers of users, targets, and cooperative base stations increase, model-based optimization becomes computationally expensive, while learning-based approaches require substantial interaction data and training overhead. The different time scales of trajectory updates and wireless-resource adaptation further complicate practical deployment. The unresolved problem is therefore to design scalable online methods that remain effective under channel uncertainty, unpredictable target motion, energy constraints, interference, and clutter.