黄国策¹,
王桂胜^1,,,
任清华¹,
董淑福¹,
高维廷¹,
魏帅²
1.空军工程大学信息与导航学院 西安 710077
2.95910部队 ??酒泉 ??735000
基金项目:国家自然科学基金(61701521)，中国博士后科学基金(2016M603044)，陕西省自然科学基金(2018JQ6074)

详细信息

作者简介:黄国策：男，1962年生，博士，教授，研究方向为军事航空通信、短波组网
王桂胜：男，1992年生，博士生，研究方向为军事航空通信、通信抗干扰、认知无线网络
任清华：男，1967年生，教授，研究方向为军事航空通信、变换域通信
董淑福：男，1971年生，教授，研究方向为军事航空通信、短波组网
高维廷：男，1984年生，博士，研究方向为电磁频谱管理
魏帅：女，1993年生，硕士，研究方向为多目标跟踪识别

通讯作者:王桂胜　wgsfuyun@163.com

中图分类号:TN92

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出版历程

收稿日期:2018-09-18
修回日期:2019-03-26
网络出版日期:2019-04-23
刊出日期:2019-08-01

Adaptive Recognition Method for Unknown Interference Based on Hilbert Signal Space

Guoce HUANG¹,
Guisheng WANG^1,,,
Qinghua REN¹,
Shufu DONG¹,
Weiting GAO¹,
Shuai WEI²
1. College of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
2. The 95910 Troop, Jiuquan 735000, China
Funds:The National Natural Science Foundation of China (61701521), The Postdoctoral Science Foundation of China (2016M603044), The Shaanxi Province Natural Science Foundation (2018JQ6074)


摘要
摘要:针对大样本下未知干扰类型的分类识别问题，该文提出一种基于信号特征空间的未知干扰自适应识别方法。首先，基于Hilbert信号空间理论对干扰信号进行处理，建立干扰信号特征空间，进而利用投影定理对未知干扰进行最佳逼近，提出基于信号特征空间的概率神经网络(PNN)分类算法，并设计了未知干扰分类识别器的处理流程。仿真结果表明，与两种传统方法相比，该方法在已知干扰的分类精度方面分别提高了12.2%和2.8%；满足条件的未知干扰最佳逼近效果随功率强度呈线性变化，设计的分类识别器在满足最佳逼近的各类干扰中总体识别率达到91.27%，处理干扰识别的速度明显改善；在信噪比达到4 dB时，对未知干扰识别准确率达到92%以上。
关键词:无人机通信/
未知干扰/
自适应识别/
Hilbert信号空间/
概率神经网络
Abstract:In order to solve the problem of classification and recognition of unknown interference types under large samples, an adaptive recognition method for unknown interference based on signal feature space is proposed. Firstly, the interference signal is processed and the interference signal feature space is established with the Hilbert signal space theory. Then the projection theorem is used to approximate the unknown interference. The classification algorithm based on signal feature space with Probabilistic Neural Network (PNN) is proposed, and the processing flow of unknown interference classifier is designed. The simulation results show that compared with two kinds of traditional methods, the proposed method improves the classification accuracy of the known interference by 12.2% and 2.8% respectively. The optimal approximation effect of the unknown interference varies linearly with the power intensity in the condition, and the overall recognition rate of the designed classifier reaches 91.27% in the various types of interference satisfying the optimal approximation, and the speed of processing interference recognition is improved significantly. When the signal-to-noise ratio reaches 4 dB, the accuracy of unknown interference recognition is more than 92%.
Key words:Unmanned aerial vehicle communications/
Unknown interference/
Adaptive recognition/
Hilbert signal space/
Probabilistic Neural Network (PNN)



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