Causal-Inspired Unsupervised Dynamic-Attribute Feature Disentanglement Network for HRRP Target Recognition

High-Resolution Range Profile (HRRP), which contains rich discriminative information about targets, is widely used in radar automatic target recognition. However, the aspect sensitivity of HRRP data limits recognition performance in scenarios with missing or varying aspects, while noise and other disturbances also distort HRRP waveforms, impairing recognition. To address these issues, inspired by causal theory, this paper proposes an HRRP target recognition framework comprising an Unsupervised Dynamic-Attribute Feature Disentanglement Network (UDAFD-Net) and an HRRP Recognition Network (HRRP-RecNet) in cascade. UDAFD-Net employs an asymmetric variational inference architecture to decompose HRRP sequence samples into dynamic features (related to aspect variations, noise, and other disturbances) and attribute features (solely linked to target types). A counterfactual regularization constraint is integrated into the Evidence Lower Bound (ELBO) loss to block spurious correlations between dynamic and attribute features, ensuring their independence. Finally, the disentangled attribute features and their corresponding type labels are used to train HRRP-RecNet, ensuring that recognition results depend only on the target's intrinsic attributes, free from irrelevant factors. Experiments on the aircraft electromagnetic simulation dataset and the measured dataset demonstrate that the proposed method achieves higher accuracy and stronger robustness in HRRP target recognition without requiring any aspect labels or prior knowledge of disturbances.