Abstract:With the rapid development of intelligent technology, multi-target cooperative systems have demonstrated significant technical value and application potential in complex dynamic environments. In response to the characteristics of a large number of target groups, dense spatial distribution, and highly coordinated movement, this study proposes a target situation awareness algorithm based on multi-sensor collaboration. The system first achieves precise target matching in complex environments by constructing a point set graph structure, geometric transformation, and matching optimization algorithm. On this basis, it integrates multi-view geometry principles and camera imaging models to construct a global observation view, transforming the analysis of target spatial distribution into a triangulation positioning problem, which significantly improves measurement accuracy and effectively addresses the problem of target missed detection in multi-target situation awareness. Experimental results show that this method has high practicality and reliability in complex dynamic environments: with a position error range of (0,500) m and an attitude error range of (-60°, 60°), the recognition accuracy rate remains above 95.18%, the number estimation error is controlled within 1.19%, and the target distribution error is 1.11 m.