基于同步压缩小波变换的主信号抑制技术

吴龙文; 牛金鹏; 王昭; 何胜阳; 赵雅琴

doi:10.11999/JEIT190650

基于同步压缩小波变换的主信号抑制技术

doi: 10.11999/JEIT190650

1.
哈尔滨工业大学电子与信息工程学院哈尔滨 150001
2.
中国电子科技集团公司第二十九研究所成都 610036

基金项目: 国家自然科学基金(61671185)

详细信息

作者简介:
吴龙文：男，1988年生，工程师，研究方向为辐射源个体识别

牛金鹏：男，1997年生，硕士生，研究方向为辐射源个体识别

王昭：男，1995年生，工程师，研究方向为辐射源个体识别

何胜阳：男，1983年生，高级工程师，研究方向为无线光通信

赵雅琴：女，1976年生，教授，研究方向为辐射源识别和光通信

通讯作者:
赵雅琴　yaqinzhao@hit.edu.cn

中图分类号: TN971
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出版历程
- 收稿日期: 2019-08-28
- 修回日期: 2020-05-05
- 网络出版日期: 2020-05-17
- 刊出日期: 2020-08-18

Primary Signal Suppression Based on Synchrosqueezed Wavelet Transform

1.
School of Electronics & Information Engineering, Harbin Institute of Technology, Harbin 150001, China
2.
The 29th Research Institute of China Electronics Technology Group Corporation, Chengdu 610036, China

Funds: The National Natural Science Foundation of China (61671185)

摘要

摘要:
在辐射源个体识别(SEI)技术中，能量较高的主信号往往导致微弱个体特征稳定性降低，进而影响最终的个体识别效果。为了解决该问题并提升辐射源个体识别性能，该文提出基于同步压缩小波变换的主信号抑制技术。首先，利用静态小波变换完成对带噪信号的去噪预处理；然后，利用同步压缩小波变换完成对主信号的检测和抑制，并以均方根误差和皮尔逊相关系数为数值指标，验证算法的有效性；最后，在主信号抑制的基础上，利用分形理论中盒维数完成对信号的特征提取，并利用单核支持向量机验证个体识别性能。实验结果表明，与主信号抑制之前相比，主信号抑制算法下个体识别率提升了10%左右，验证了同步压缩小波变换的主信号抑制算法对辐射源个体识别率提升的有效性。
- 辐射源个体识别 /
- 主信号抑制 /
- 同步压缩小波变换 /
- 特征提取
Abstract:
In Specific Emitter Identification (SEI), the stability of individual features and final correct identification rate are always declined due to the influence of the primary signal with high energy on the individual features. To solve the problem above, a primary signal suppression algorithm based on synchrosqueezed wavelet transform is exploited for specific emitter identification in this paper. Firstly, a denoising method based on stationary wavelet transform is applied to preprocess the noised signal; Then, the detection and suppression of the primary signal from time-frequency distribution are developed, where root mean square error and Pearson correlation coefficient are used as numerical indicators to measure the effectiveness of the proposed primary signal suppression algorithm; Finally, a feature extraction based on box-counting dimension and a classification based on support vector machine are exploited to verify the identification performance. The simulation results show that the correct identification rate of SEI using the proposed primary signal suppression outperforms the conventional SEI with 10%, which proves the practical improvement of the proposed primary signal suppression algorithm on specific emitter identification.
- Specific Emitter Identification(SEI) /
- Primary signal suppression /
- Synchrosqueezed wavelet transform /
- Feature extraction

HTML全文

图 1 SWT分解过程示意图

下载: 全尺寸图片幻灯片

图 2 LFM信号下SST主信号抑制效果仿真

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图 3 SST主信号抑制仿真(扩大相位噪声频1偏后)

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图 4 LFM信号源个体分形盒维数特征识别结果

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图 5 实测数据特征规范化后特征分布

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表 1 加性相位噪声参数

辐射源个体	与频偏对应的相位噪声幅度(信相噪比(dB))
	f₁=±2.75 MHz	f₂=±2.80 MHz	f₃=±3.10 MHz
E₁	11.9897	12.7815	15.7918
E₂	10.4845	11.6722	16.1877
	f₂₁=±2.8 MHz	f₂₂=±2.9 MHz	f₂₃=±3.15 MHz
E₃	12.7815	14.0308	16.1394

下载: 导出CSV

表 2 实测数据特征结构与来源

特征序号	特征来源
1～4	RF-DNA^[19]
4～8	IMF-DNA^[20]
9～12	BCD^[18]
13～20	SIB^[21]

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