方坤,,
樊韬,
刘佳文,
吕思婷
西安电子科技大学综合业务网国家重点实验室 西安 710077
详细信息
作者简介:李晓辉：女，1972年生，教授，研究方向为宽带无线通信、无线资源管理
方坤：男，1996年生，硕士生，研究方向为无人机信号定位
樊韬：男，1994年生，博士生，研究方向为宽带无线通信
刘佳文：男，1995年生，博士生，研究方向为宽带无线通信
吕思婷：女，1998年生，博士生，研究方向为宽带无线通信
通讯作者:方坤 1825368900@qq.com
中图分类号:TN911.7; TN92计量
文章访问数:248
HTML全文浏览量:127
PDF下载量:48
被引次数:0
出版历程
收稿日期:2020-08-14
修回日期:2021-07-02
网络出版日期:2021-07-15
刊出日期:2021-09-16
Research on Unmanned Aerial Vehicle Location Signal Separation Algorithm Based on Support Vector Machines
Xiaohui LI,Kun FANG,,
Tao FAN,
Jiawen LIU,
Siting Lü
State Key Laboratory of ISN, Xidian University, Xi’an 710077, China
摘要
摘要:为了解决无人机(UAV)无源定位中难以从多径干扰严重的环境中提取无人机定位信号的问题，该文提出一种基于支持向量机(SVM)的无人机定位信号分离算法，在SVM模型训练时，通过计算无人机相邻数据集之间的欧氏距离获取信息熵，为SVM映射高维空间提供模型数据。在此基础上，加入映射函数阈值软边界，使模型具有参数自适应调整能力，来适应无人机运动灵活所导致的数据差异。最后构建了观测者操作特性曲线获取无人机定位信号分离结果。仿真结果表明所提算法能够有效分离无人机定位信号与噪声，在多径干扰严重的情况下具有较高的信号分离准确率。
关键词:无人机定位/
支持向量机/
信息熵/
噪声分离
Abstract:In order to solve the problem that it is difficult to extract the Unmanned Aerial Vehicle (UAV) positioning signal from the environment with severe multipath interference in the passive positioning of the UAV, a UAV positioning signal separation based on Support Vector Machines (SVM) algorithm is proposed. During the training of the SVM model, the information entropy is obtained by calculating the Euclidean distance between the adjacent data sets of the UAV, and the model data is provided for the SVM to map the high-dimensional space. On this basis, the soft boundary of the threshold of the mapping function is added to make the model have the ability to adjust parameters adaptively to adapt to the data difference caused by the flexible movement of the UAV. Finally, an observer operating characteristic curve is constructed to obtain the result of UAV positioning signal separation. The simulation results show that the proposed algorithm can effectively separate the UAV positioning signal and noise.
Key words:Unmanned Aerial Vehicle (UAV) positioning/
Support Vector Machines (SVM)/
Information entropy/
Noise separation
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