一种用于无线节点身份验证的射频指纹识别方法
By converting wireless signals into time-spectrum graphs and using feature projection and alignment-free attention weight training, the problem of feature extraction for wireless node authentication in short-frame wireless signals is solved, achieving efficient and lightweight authentication.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHANGCHUN UNIV OF SCI & TECH
- Filing Date
- 2026-05-26
- Publication Date
- 2026-07-17
AI Technical Summary
Existing radio frequency fingerprinting methods struggle to effectively extract the identity features of wireless nodes in short-frame wireless signal scenarios. Furthermore, traditional distillation methods rely on rigid feature alignment, leading to difficulties in deploying recognition models and a high false positive rate.
By converting training wireless signal samples into time-spectrum samples, using teacher and student identification models for feature projection and cross-dimensional correlation matrix normalization, alignment-free attention weights are generated, and feature distillation loss is used for training to establish a lightweight radio frequency fingerprint recognition model.
It enhances the distinguishability of wireless node identity features, reduces the misjudgment rate in short-frame wireless signal scenarios, improves the consistency of identity category determination, and maintains the model's lightweight deployment advantage.
Smart Images
Figure CN122262833B_ABST