干扰信号的干扰类型识别方法、装置、介质及设备
By using a recognition model that integrates a moving-flipping bottleneck convolution module, a multi-head attention mechanism module, and a bidirectional gated recurrent unit, the problem of low accuracy in identifying interference signals is solved, achieving higher recognition accuracy and faster training speed.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING FORESTRY UNIVERSITY
- Filing Date
- 2024-07-08
- Publication Date
- 2026-07-17
AI Technical Summary
Existing methods for identifying interference signals have low accuracy and cannot effectively identify diverse types of interference.
A recognition model consisting of a fusion of a moving-flipping bottleneck convolution module, a multi-head attention mechanism module, a bidirectional gated recurrent unit, and a fully connected layer is used to process the time-frequency image of interference signals. Through convolution operations, pooling calculations, attention operations, and recurrent unit processing, various interference types are identified.
It improves the accuracy of interference signal recognition, enhances the model's classification performance and generalization ability, simplifies the model structure, and increases training speed.
Smart Images
Figure CN118885922B_ABST