基于云模型的DSm证据建模及雷达辐射源识别方法

郭强; 何友

doi:10.11999/JEIT150053

基于云模型的DSm证据建模及雷达辐射源识别方法

doi: 10.11999/JEIT150053

郭强¹,
何友¹

基金项目:

国家自然科学基金(61102166, 61471379)和山东省优秀中青年科学家科研奖励基金(BS2013DX003)

计量
- 文章访问数: 1408
- HTML全文浏览量: 110
- PDF下载量: 796
- 被引次数: 0
出版历程
- 收稿日期: 2015-01-09
- 修回日期: 2015-03-25
- 刊出日期: 2015-08-19

DSm Evidence Modeling and Radar Emitter Fusion Recognition Method Based on Cloud Model

Guo Qiang¹,
He You¹

摘要

摘要: 为了提高雷达辐射源特征参数存在互相交叠和多个模式情况的雷达辐射源正确识别率，该文提出一种基于云模型的DSm(Dezert-Smarandache)证据建模及雷达辐射源识别方法。该方法首先将存在互相交叠和多个模式的先验雷达辐射源特征参数进行基于云模型的DSm建模，然后将含有噪声的测量信号特征参数进行基于云模型的DSm隶属度赋值，再通过隶属度与基本信度赋值的关系求得DSm模型的基本信度赋值，最后通过DSmT+PCR5的方法将多传感器测量信号的同特征的基本信度赋值进行融合，再将各特征的融合结果进行DSmT+PCR5融合得到最终的识别结果，如果仅为单传感器测量信号的特征参数，则仅将不同特征参数的基本信度赋值进行DSmT+PCR5得到融合识别结果。最后通过多种情况下的仿真实验，验证了该文方法的优越性。
- 雷达辐射源识别 /
- 信息融合 /
- 云模型 /
- 基本信度赋值 /
- Dezert-Smarandache理论
Abstract: To improve the correct radar emitter recognition rate in cases that radar emitter characteristic parameters are overlapped with each other and existence of multiple modes, a DSm (Dezert-Smarandache) evidence modeling and radar emitter fusion recognition method based on cloud model is proposed. First, the radar emitter characteristic parameters which are overlapped and have multiple modes are modeled in DSm frame based on cloud model, then the degree of membership of unkonwn radar emitter signal belonging to prior radar types of each characteristic parameter is obtained by this model. Second, the basic belief assignments in DSm frame based on cloud model are obtained by the relationship between degree of membership and basic belief assignments. Thirdly, the basic belief assignments of the same characteristic parameters of multi-source unkown emitter signal are fused by DSmT+PCR5, then the fusion results of each characteristic parameters are fused to get the final recognition results. If there are only single-source unknown signal characteristic parameters, the basic belief assignments of each characteristic parameter are fused by DSmT+PCR5 to get the final recognition results. Finally, through the simulation experiments in multiple conditions, the superiority of the proposed method is testified well.
- Radar emitter recognition /
- Information fusion /
- Cloud model /
- Basic belief assignments /
- Dezert- Smarandache theory

HTML全文

参考文献(30)

刘海军, 柳征, 姜文利, 等. 基于联合参数建模的雷达辐射源识别方法[J]. 宇航学报, 2011, 32(1): 142-149.

Liu Hai-jun, Liu Zheng, Jiang Wen-li, et al.. A joint-parameter modeling based radar emitter identification method[J]. Journal of Astronautics, 2011, 32(1): 142-149.

徐璟, 何明浩, 冒燕, 等. 基于优化算法的雷达辐射源信号识别方法及性能[J]. 现代雷达, 2014, 36(10): 33-37.

Xu Jing, He Ming-hao, Mao Yan, et al.. Radar emitter recognition method based on optimization algorithm and performance[J]. Modern Radar, 2014, 36(10): 33-37.

杨承志, 吴宏超, 贾苹, 等. 基于云模型和支持向量机的辐射源识别算法[J]. 现代雷达, 2013, 35(10): 41-44.

Yang Cheng-zhi, Wu Hong-chao, Jia Ping, et al.. Approach based on cloud model and SVM for emitter identification[J]. Modern Radar, 2013, 35(10): 41-44.

史亚, 姬红兵, 朱明哲, 等. 多核融合框架下的雷达辐射源个体识别[J]. 电子与信息学报, 2014, 36(10): 2484-2490.

Shi Ya, Ji Hong-bing, Zhu Ming-zhe, et al.. Specific radar emitter identification in multiple kernel fusion framework [J]. Journal of Electronics Information Technology, 2014, 36(10): 2484-2490.

刘凯, 王杰贵, 李俊武. 基于区间灰关联的雷达辐射源识别新方法[J]. 火力与指挥控制, 2013, 38(7): 20-23.

Liu Kai, Wang Jie-gui, and Li Jun-wu. A new method based on interval grey association for radar emitter recognition[J]. Fire Control Command Control, 2013, 38(7): 20-23.

关欣, 孙贵东, 郭强, 等. 基于区间数和证据理论的雷达辐射源参数识别[J]. 系统工程与电子技术, 2014, 36(7): 1269-1274.

Guan Xin, Sun Gui-dong, Guo Qiang, et al.. Radar emitter parameter recognition based on interval number and evidence theory[J]. Systems Engineering and Electronics, 2014, 36(7): 1269-1274.

徐志军, 陈志伟, 王金明, 等. 基于功放特性的辐射源识别的改进方法[J]. 南京邮电大学学报(自然科学版), 2013, 33(6): 54-58.

Xu Zhi-jun, Chen Zhi-wei, Wang Jin-ming, et al.. An improved method for emitter identification based on character of power amplifier[J]. Journal of Nanjing University of Posts and Telecommunications (Natural Science), 2013, 33(6): 54-58.

公绪华, 袁振涛, 谭怀英. 基于GMM和神经网络的辐射源识别方法[J]. 雷达科学与技术, 2014, 12(5): 482-486.

Gong Xu-hua, Yuan Zhen-tao, and Tan Huai-ying. The methods based on the GMM and neural network for recognition of emitters[J]. Radar Science and Technology, 2014, 12(5): 482-486.

刘海军, 柳征, 姜文利, 等. 一种基于云模型的辐射源识别方法[J]. 电子与信息学报, 2009, 31(9): 2079-2083.

Liu Hai-jun, Liu Zheng, Jiang Wen-li, et al.. A method for emitter recognition based on cloud model[J]. Journal of Electronics Information Technology, 2009, 31(9): 2079-2083.

付耀文, 杨威, 庄钊文. 证据建模研究综述[J]. 系统工程与电子技术, 2013, 35(6): 1160-1167.

Fu Yao-wen, Yang Wei, and Zhuang Zhao-wen. Review on evidence modeling[J]. Systems Engineering and Electronics, 2013, 35(6): 1160-1167.

Smarandache F and Dezert J. Advances and Applications of DSmT for Information Fusion[M]. Vol. 3, Rehoboth, USA: American Research Press, 2009: 54-58.

Wang Guo-yin, Xu Chang-lin, and Li De-yi. Generic normal cloud model[J]. Information Sciences, 2014, 280: 1-15.

秦丽, 李兵. 一种基于云模型的不确定性数据的建模与分类方法[J]. 计算机科学, 2014, 41(8): 233-240.

Qin Li and Li Bing. Novel method of uncertain data modeling and classification based on cloud model[J]. Computer Science, 2014, 41(8): 233-240.

徐晓滨, 文成林, 刘荣利. 基于随机集理论的多源信息统一表示与建模方法[J]. 电子学报, 2008, 36(6): 1174-1181.

Xu Xiao-bin, Wen Cheng-lin, and Liu Rong-li. The unified method of describing and modeling multisource information based on random set theory[J]. Acta Electronica Sinica, 2008, 36(6): 1174-1181.

彭冬亮, 文成林, 徐晓滨, 等. 随机集理论及其在信息融合中的应用[J]. 电子与信息学报, 2006, 28(11): 2199-2204.

Peng Dong-liang, Wen Cheng-lin, Xu Xiao-bin, et al.. Random set and its application[J]. Journal of Electronics Information Technology, 2006, 28(11): 2199-2204.

李新德, 杨伟东, 吴雪建, 等. 一种快速分层递阶DSmT 近似推理融合方法(B)[J]. 电子学报, 2011, 3(s1): 31-36.

Li Xin-de, Yang Wei-dong, Wu Xue-jian, et al.. A fast approximate reasoning method in hierarchical DSmT(B)[J]. Acta Electronica Sinica, 2011, 3(s1): 31-36.

施引文献

资源附件(0)

访问统计