PASM-Net: A Coordinate-Aware Manifold-Regularized Framework for Open-Set UAV RF Fingerprint Identification

Abstract

To address the challenges of classifying known categories and rejecting unknown categories in the radio-frequency fingerprinting of uncooperative UAVs in open, low-altitude environments, this paper proposes PASM-Net, an open-set recognition method based on coordinate awareness and manifold continuity regularization. Existing deep learning-based RFFI methods still suffer from two shortcomings in open scenarios: First, while conventional convolutions and global pooling yield compact representations, they may weaken position-related information in the time–frequency spectrum, thereby affecting the model’s ability to characterize frequency-hopping trajectories and local time–frequency structures; on the other hand, decision mechanisms that rely solely on Softmax classifiers or simple distance metrics are susceptible to signal amplitude fluctuations and intra-class distribution dispersion, leading to the misclassification of unknown samples into known classes. To address these issues, this paper designs the PASM-Net feature-learning framework. First, the model employs multiscale hollow convolutions to extract local time–frequency features across different receptive fields and introduces a coordinate attention mechanism (Coordinate Attention, CoordAtt) prior to global pooling, thereby enhancing the model’s ability to represent position-related information in both the time and frequency domains. Second, the model introduces manifold continuity regularization (MCR) in the fused semantic space. By constraining feature variations within local neighborhoods via a Laplacian regularization term, MCR reduces the dispersion of known-class feature distributions. Finally, the model employs ArcFace to enhance the angular separability among known classes and performs open-set classification based on the cosine distance between test samples and the centers of known classes. The experimental results across six open-set scenarios on the DroneRFb-Spectra dataset showed that PASM-Net achieved an average true unknown rate (TUR) of 99.48%, an unknown accuracy of 97.9%, and an unknown-class precision (UP) of 87.3%, while maintaining a high performance in known-class recognition. Under the current experimental setup, this effectively improves the trade-off between known-class classification and unknown-class rejection.