Resource Optimization and Performance Improvement of Multi-Cluster Target Tracking Algorithm

The tracking of multi-cluster targets plays a critical role in monitoring and responding to military threats. However, the tracking process faces significant challenges, including excessive resource consumption and reduced tracking performance. This study proposes an algorithm to address the resource consumption issue in multi-cluster target tracking and improve the tracking performance. The key techniques include tracking area partitioning, cluster-target association processing, tentative tracking, and group tracking. Each technique is specifically designed to optimize resource utilization and ensure reliable tracking performance. The simulation results demonstrate that the proposed algorithm outperforms existing methods in terms of resource efficiency and tracking performance in multi-cluster target tracking.