ArticleSensors (Basel, Switzerland)2022
Low SNR Multi-Emitter Signal Sorting and Recognition Method Based on Low-Order Cyclic Statistics CWD Time-Frequency Images and the YOLOv5 Deep Learning Model.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- An Automatic Modulation Recognition Method Based on the Multimodal Kernel Harmonic Feature Fusion Network.Sensors (Basel, Switzerland) · 2025Article
- DFN-YOLO: Detecting Narrowband Signals in Broadband Spectrum.Sensors (Basel, Switzerland) · 2025Article
- Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
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Abstract
It is difficult for traditional signal-recognition methods to effectively classify and identify multiple emitter signals in a low SNR environment. This paper proposes a multi-emitter signal-feature-sorting and recognition method based on low-order cyclic statistics CWD time-frequency images and the YOLOv5 deep network model, which can quickly dissociate, label, and sort the multi-emitter signal features in the time-frequency domain under a low SNR environment. First, the denoised signal is extracted based on the low-order cyclic statistics of the typical modulation types of radiation source signals. Second, the time-frequency graph of multisource signals was obtained through CWD time-frequency analysis. The cyclic frequency was controlled to balance the noise suppression effect and operation time to achieve noise suppression of multisource signals at a low SNR. Finally, the YOLOv5s deep network model is used as a classifier to sort and identify the received signals from multiple radiation sources. The method proposed in this paper has high real-time performance. It can identify the radiation source signals of different modulation types with high accuracy under the condition of a low SNR.
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