基于通信域和雷达域融合特征的无人机集群类型识别算法
TN911; 目前已有无人机类型识别算法仅通过通信域或雷达域的信号特征实现单个无人机类型的识别,存在识别准确率低等问题.提出了一种基于通信信号和雷达信号融合特征的无人机集群类型识别算法.首先,提取集群通信信号的高阶累积量和瞬时特征统计量,并融合雷达航迹特征构建无人机集群特征矩阵.其次,提出一种改进的特征选择算法—二次筛选的近邻成分分析,对融合特征矩阵进行降维.最后,利用稀疏自编码器网络进行集群类型的识别.仿真结果表明,该算法显著降低了集群特征矩阵的维度(仅为原始矩阵维度的27％),同时在信噪比为0 dB时,对5种集群类型识别的正确率可达88％....
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| Published in | 系统工程与电子技术 Vol. 45; no. 12; pp. 3734 - 3742 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
南京航空航天大学电子信息工程学院,江苏南京 210016
01.12.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-506X |
| DOI | 10.12305/j.issn.1001-506X.2023.12.03 |
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| Abstract | TN911; 目前已有无人机类型识别算法仅通过通信域或雷达域的信号特征实现单个无人机类型的识别,存在识别准确率低等问题.提出了一种基于通信信号和雷达信号融合特征的无人机集群类型识别算法.首先,提取集群通信信号的高阶累积量和瞬时特征统计量,并融合雷达航迹特征构建无人机集群特征矩阵.其次,提出一种改进的特征选择算法—二次筛选的近邻成分分析,对融合特征矩阵进行降维.最后,利用稀疏自编码器网络进行集群类型的识别.仿真结果表明,该算法显著降低了集群特征矩阵的维度(仅为原始矩阵维度的27％),同时在信噪比为0 dB时,对5种集群类型识别的正确率可达88％. |
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| AbstractList | TN911; 目前已有无人机类型识别算法仅通过通信域或雷达域的信号特征实现单个无人机类型的识别,存在识别准确率低等问题.提出了一种基于通信信号和雷达信号融合特征的无人机集群类型识别算法.首先,提取集群通信信号的高阶累积量和瞬时特征统计量,并融合雷达航迹特征构建无人机集群特征矩阵.其次,提出一种改进的特征选择算法—二次筛选的近邻成分分析,对融合特征矩阵进行降维.最后,利用稀疏自编码器网络进行集群类型的识别.仿真结果表明,该算法显著降低了集群特征矩阵的维度(仅为原始矩阵维度的27％),同时在信噪比为0 dB时,对5种集群类型识别的正确率可达88％. |
| Author | 张书衡 刘永凯 翟茹萍 |
| AuthorAffiliation | 南京航空航天大学电子信息工程学院,江苏南京 210016 |
| AuthorAffiliation_xml | – name: 南京航空航天大学电子信息工程学院,江苏南京 210016 |
| Author_FL | ZHAI Ruping ZHANG Shuheng LIU Yongkai |
| Author_FL_xml | – sequence: 1 fullname: ZHANG Shuheng – sequence: 2 fullname: ZHAI Ruping – sequence: 3 fullname: LIU Yongkai |
| Author_xml | – sequence: 1 fullname: 张书衡 – sequence: 2 fullname: 翟茹萍 – sequence: 3 fullname: 刘永凯 |
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| DocumentTitle_FL | Identification of UAV swarm type based on fusion features of communication and radar domain |
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| Keywords | 无人机集群类型识别 高阶累积量 unmanned aerial vehicle(UAV)swarm type identification 特征选择 瞬时特征统计量 雷达航迹 high-order cumulant instantaneous feature statistics radar trajectory feature selection |
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| Title | 基于通信域和雷达域融合特征的无人机集群类型识别算法 |
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