A Hybrid Architecture Multistatic Track Initiation Method Based on Spatiotemporal Feature Fusion Network

This paper proposes an intelligent track initiation method based on spatio-temporal feature fusion network for multistatic radar systems, addressing the challenge of multi-target track initiation in complex electromagnetic environments. The technique leverages a combination of a Spatio-Temporal Feature-driven Threshold Network (STFTN) and a Spatio-Temporal Graph Convolutional Network (STGCN) within a hybrid architecture, combining the computational efficiency of distributed processing with the information richness of centralized processing. Initially, a coarse screening is performed locally at the sub-processor using the speed limit method. Subsequently, relevant data is sent to a central processor where the STFTN dynamically generates intelligent thresholds to optimize the correlation range for candidate tracks. Finally, motion features of candidate tracks are extracted and fed into the STGCN in the central processor for secondary intelligent judgment, effectively distinguishing true tracks from false alarms. Simulation results demonstrate that the proposed algorithm achieves higher track initiation rates and fewer false tracks compared with traditional methods.