基于时频表征优化与网络重构的宽带信号检测识别方法
By dividing the time-frequency characteristics of broadband signals into an energy-phase joint domain and an energy domain, and reconstructing them using ResNet50 and CV-ResNet50 backbone networks, as well as Sparse R-CNN and DINO models, the problem of discarding phase information in existing methods is solved, and more efficient broadband signal detection and recognition are achieved.
Patent Information
- Application Number
- CN202610714409.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-22
- Publication Date
- 2026-07-17
- Estimated Expiration
- 2046-05-22
AI Technical Summary
Existing broadband signal detection and recognition methods discard phase information when processing complex time-frequency spectra, leading to a decrease in the performance of modulation recognition models, and the impact of different signal representation methods on task performance has not been thoroughly explored.
By dividing the ten time-frequency feature representations into the energy-phase joint domain and the energy domain, and inputting them into the ResNet50 and CV-ResNet50 backbone networks, and combining the Sparse R-CNN model and the DINO model for target detection, a common basis loss is defined for model update, thereby achieving accurate recognition of the detected signal.
It effectively adapts to different styles of time-frequency feature representation, improves the accuracy and robustness of signal detection, overcomes the information loss caused by traditional power conversion, and improves the performance of broadband signal detection and recognition.
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Abstract
Citation Information
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