UAV-Assisted Sensing Intelligence for Remote State Estimation: A Joint Scheduling and Matching Approach

This paper investigates remote state estimation in cyber-physical systems (CPS) where direct and reliable communication between distributed sensors and the control center is unavailable due to the lack of infrastructure. In such scenarios, unmanned aerial vehicles (UAVs) can be deployed as mobile data collectors to facilitate information transfer. While joint UAV trajectory and resource allocation problems have been widely studied in various contexts, adapting these to minimize estimation error in remote state estimation of linear CPS—accounting for the nonlinear evolution of error covariances and spatio-temporal coupling—presents distinct challenges. To tackle this, we formulate a joint optimization problem and decompose it into UAV path planning and UAV-sensor resource allocation. The path planning problem is modeled as a Markov Decision Process, where we establish the existence and periodicity of the optimal policy, and the resource allocation is formulated as a many-to-one matching game. We jointly solve these sub-problems via an alternating optimization algorithm that combines dynamic programming with a deferred acceptance-based matching scheme, and we prove the stability of the resulting matching. Simulation results confirm the periodic structure of the optimal path and demonstrate the effectiveness of the proposed method in reducing estimation error compared with other representative matching-based schemes.