Multiview Adaptive Hypergraph for Space Micromotion Target Recognition Based on Networked Radar Systems

Space micro-motion target recognition (SMMTR) is a key technology in space defense systems. Unlike monostatic radars, which provide only a single observation dimension, the networked radar systems offer richer information and significantly enhance target recognition performance. Traditional graph neural networks can only model binary relations, whereas hypergraph networks can effectively capture high-order complex interaction relations by allowing multiple nodes to share the same hyperedge, thereby extracting more accurate features in complex systems. In this paper, a multi-view adaptive hypergraph convolutional network (MVA-HGCN) architecture is proposed. The MVA-HGCN adaptively learns the hypergraph structure from two perspectives: time steps and radar entities. First, the multi-scale temporal adaptive hypergraph convolutional network (MSTA-HGCN) treats time steps as hypergraph nodes at different scales. This module captures more comprehensive time-varying micro-motion features across multiple temporal resolutions, reflecting the dynamic evolution of the target. Second, the radar entity adaptive hypergraph convolutional network (REA-HGCN) treats each radar as a hypergraph node. This module adaptively captures the potential complex spatial dependencies from the perspective of multi-sensor collaborative observation. Extensive experiments demonstrate that the proposed MVA-HGCN achieves reliable SMMTR performance even under harsh conditions of low signal-to-noise ratio (SNR) and low pulse repetition frequency (PRF). Specifically, it achieves a recognition accuracy of 93.89% at 5 dB SNR and 100 Hz PRF, representing a 4.78% improvement over the best existing method.