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

被引：4

作者：

Huang, Dingkun ^{[1
]}

Yan, Xiaopeng ^{[1
]}

Hao, Xinhong ^{[1
]}

Dai, Jian ^{[1
]}

Wang, Xinwei ^{[1
]}

机构：

[1] Beijing Inst Technol, Sch Mechatron Engn, Technol Electromech Dynam Control Lab, Beijing 100081, Peoples R China

来源：

SENSORS | 2022年 / 22卷 / 20期

基金：

中国国家自然科学基金;

关键词：

cyclic stationary analysis; CWD time-frequency analysis; noise suppression; YOLOv5; model; radiation source signal sorting and identification; WAVE-FORM RECOGNITION; MODULATION RECOGNITION;

D O I：

10.3390/s22207783

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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.

引用

页数：22