Joint multi-dimensional resource scheduling for cooperative tracking of multiple LEO targets via space-based radar networks

Appropriate resource scheduling is the key to achieving the best performance of Low Earth-Orbit (LEO) Multi-Target Tracking (MTT) for the Space-based Radar Networks (SBRN) system. The high-velocity nature of satellites imposes high demands on the convergence rate of filtering and tracking accuracy while escalating the complexity of dynamic scheduling. To address these challenges, this paper utilizes Two-Line Elements (TLE) information to fully exploit the limited power-aperture resource of space-based Colocated Multiple-Input Multiple-Output (C-MIMO) radar and constructs a Walker constellation SBRN system. On this basis, a cognitive distributed cooperative tracking framework with fusion feedback mechanism is proposed. Each node can serve as the fusion center, achieving optimal fusion through the Fast Covariance Intersection (FCI) criterion while reducing the communication requirements. The global outcomes are fed back to all local nodes which can hasten the convergence rate of target state estimation. To maximize the global MTT performance, a closed-loop Joint Multi-Dimensional Resource Scheduling (JMDRS) strategy that considers multi-coverage conditions and visible windows is established. Moreover, the Posterior Cramér-Rao Lower Bound under Global Fusion Feedback (GF-PCRLB) is derived to provide a quantifiable metric for the overall performance. Finally, a fast suboptimal solution to the nonconvex model is proposed based on cross-iterative dimension reduction and variable decoupling. It introduces the tracking Efficiency-to-Cost Ratio (ECR) to jointly decide the beam pointing as well as the transmit power and fusion weight of each beam. Numerical results demonstrate that the proposed method significantly outperforms the existing approaches in enhancing the MTT performance of the SBRN system.