基于有源无源特征的飞机目标识别方法研究
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摘要
常规防空雷达由于受固有性能等影响,其飞机目标自动分类与识别一直是目标识别研究领域的难点。目前,国内装备大量的常规防空雷达,如果能在常规防空雷达目标识别上取得突破,将具有重大的军事与经济意义。本文通过分析不同体制雷达在目标识别上的优势和局限性,提出把常规防空雷达回波特征和电子支援侦察系统得到的机载辐射源特征联合起来,构造合理的识别系统的新思路,以此来满足不同背景下对飞机目标识别的需求。在现代战场上,随着电子对抗技术的广泛适用,传感器所提供的信息往往是不精确、不可靠的,如何能真实的体现传感器的可信度对于系统的有效性和可靠性非常重要。在信息融合方面,如何能把模糊逻辑、神经网络、专家系统等智能技术有机结合在一起也是一个重要的发展方向。本文主要工作是研究提取飞机目标的调制周期特征,近似熵、范数熵,获取传感器可信度以及构造实现目标识别的智能化类型融合模型和算法。
由于飞机旋转部件对雷达电磁波调制会产生周期调制特征,所以常规防空雷达选取周期调制特征来识别飞机目标是比较有效的。本文研究了直升机、螺旋桨飞机和涡扇喷气飞机回波调制特性的参数模型,分析了这三类飞机所产生的回波信号特点,采用复AR频域双谱切片法来有效获取目标回波的周期调制特征。
由于传统方法等在提取机载雷达辐射源信号特征的时候存在着一定的不足,本文提出把近似熵和范数熵作为飞机目标识别的无源特征参数,给出了提取相应特征熵的算法,实现对机载雷达辐射源信号的识别,通过和机载雷达类型的匹配,实现对目标的识别。
由于传感器受自身和环境等因素的影响,所测的信息往往不准确、不可靠,为了使多传感器信息融合结果更加可靠,本文把传感器可信度引入融合算法中。本文将传感器可信度分为统计可信度和环境可信度。研究采用BP神经网络获得传感器的统计可信度,用自适应模糊神经网络获得传感器的环境可信度,使传感器可信度更符合实际情况。
本文研究了基于神经网络、模糊推理与专家系统的多极神经网络类型融合系统,包括传感器子网和融合子网。传感器子网是一种基于专家规则的模糊神经网络,网络结构和各个节点都有确切的含义。传感器子网通过多个传感器获得的目标特征信息,实现对各类目标的置信度分配,然后融合子网结
Because the convention air defense radar influences by its inherent performance, its airplane goal automatic sorting and the recognition always is very difficulty in the target identification research area. At present, massive convention air defense radars are equiped in domestic, if obtaining the breakthrough in the convention air defense radar’s target identification will have the significant military and the economical significance. This article, through analysis different system radar’s superiority and limitation in target identification, proposes to unite the convention air defense radar’s echo characteristic and the electronic support sensory system’s aircraft-borne radiant characteristic, structure reasonable recognition system. It can satisfie the airplane target identification demand under the different background. In the modern battlefield, with the electronic countermeasures widely used, the information provided by the sensors is not precise, unreliable. How can manifest the sensor’s confidence level really is extremely important in insuring the system’s validity and reliability. In the aspect of information fusion, how can unite intelligent technologys for example fuzzy logic, neural network, expert system is also an important development direction. In this article, the prime task is the reasarch of extraction airplane’s periodic signature of modulation, the approximate entropy, the norm entropy, obtaining the sensor confidence level as well as structuring realization target identification intellectualized type fusion model and the algorithm.
Because the airplane revolving part can make the radar electromagnetic wave have the cyclical modulation feature, therefore the convention air defense radar selecting the cyclical modulation feature is quite effective in distinguishing the airplane. This paper studies the helicopter, the propeller-driven aircraft and the turbofan jet airplane echo modulating feature parameter model and analyses the echo signal feature. It gains the target echo’s the cyclical modulation feature by using the complex AR frequency range bispectrum slice.
Because traditional method in extraction airborne radar’s radiant signal feature time has certain insufficiency, this paper proposes the approximate entropy and the norm entropy as the passive feature parameter in the airplane
由于飞机旋转部件对雷达电磁波调制会产生周期调制特征,所以常规防空雷达选取周期调制特征来识别飞机目标是比较有效的。本文研究了直升机、螺旋桨飞机和涡扇喷气飞机回波调制特性的参数模型,分析了这三类飞机所产生的回波信号特点,采用复AR频域双谱切片法来有效获取目标回波的周期调制特征。
由于传统方法等在提取机载雷达辐射源信号特征的时候存在着一定的不足,本文提出把近似熵和范数熵作为飞机目标识别的无源特征参数,给出了提取相应特征熵的算法,实现对机载雷达辐射源信号的识别,通过和机载雷达类型的匹配,实现对目标的识别。
由于传感器受自身和环境等因素的影响,所测的信息往往不准确、不可靠,为了使多传感器信息融合结果更加可靠,本文把传感器可信度引入融合算法中。本文将传感器可信度分为统计可信度和环境可信度。研究采用BP神经网络获得传感器的统计可信度,用自适应模糊神经网络获得传感器的环境可信度,使传感器可信度更符合实际情况。
本文研究了基于神经网络、模糊推理与专家系统的多极神经网络类型融合系统,包括传感器子网和融合子网。传感器子网是一种基于专家规则的模糊神经网络,网络结构和各个节点都有确切的含义。传感器子网通过多个传感器获得的目标特征信息,实现对各类目标的置信度分配,然后融合子网结
Because the convention air defense radar influences by its inherent performance, its airplane goal automatic sorting and the recognition always is very difficulty in the target identification research area. At present, massive convention air defense radars are equiped in domestic, if obtaining the breakthrough in the convention air defense radar’s target identification will have the significant military and the economical significance. This article, through analysis different system radar’s superiority and limitation in target identification, proposes to unite the convention air defense radar’s echo characteristic and the electronic support sensory system’s aircraft-borne radiant characteristic, structure reasonable recognition system. It can satisfie the airplane target identification demand under the different background. In the modern battlefield, with the electronic countermeasures widely used, the information provided by the sensors is not precise, unreliable. How can manifest the sensor’s confidence level really is extremely important in insuring the system’s validity and reliability. In the aspect of information fusion, how can unite intelligent technologys for example fuzzy logic, neural network, expert system is also an important development direction. In this article, the prime task is the reasarch of extraction airplane’s periodic signature of modulation, the approximate entropy, the norm entropy, obtaining the sensor confidence level as well as structuring realization target identification intellectualized type fusion model and the algorithm.
Because the airplane revolving part can make the radar electromagnetic wave have the cyclical modulation feature, therefore the convention air defense radar selecting the cyclical modulation feature is quite effective in distinguishing the airplane. This paper studies the helicopter, the propeller-driven aircraft and the turbofan jet airplane echo modulating feature parameter model and analyses the echo signal feature. It gains the target echo’s the cyclical modulation feature by using the complex AR frequency range bispectrum slice.
Because traditional method in extraction airborne radar’s radiant signal feature time has certain insufficiency, this paper proposes the approximate entropy and the norm entropy as the passive feature parameter in the airplane
引文
1 许洪波, 胡伟稿, 万山虎. 基于有源/无源特征识别飞机目标的初探. 雷达与对抗. 2004,(3):16~19
2 李永田, 刁立峰. 航空电子系统多传感器数据融合的目标属性评估. 电光与控制. 2003,10(3):45~47
3 徐毅, 金德琨, 敬忠良. 数据融合研究的回顾与展望. 信息与控制. 2002,31(3):250~255
4 Mark R.Bell, Robert A.Grubbs. JEM Modeling and Measurement for Radar Target Identification. IEEE Trans. on Aerospace and Electronic Systems. 1993,29(1):73~87
5 K.K.Chan, F.Tremblay, S.David. Physical Optics Analysis of Rotating in a Cylinder. Proceeding of the IEEE. 1995:34~37
6 I.Jouny, E.D.Garber, R.L.Moses. Radar Target Identification Using the Bispectrum:A Comparative Study. IEEE Trans. on Aerospace and Electronic Systems. 1995,31(1):69~78
7 M.R.Raghuveer, C.L.Nikias. A Parametic Approach To Bispetrum Estimation. Proceeding of the IEEE. 1884,38(1):1~4
8 丁建江, 张贤达. 防空雷达飞机目标调制周期特征的研究. 电子学报. 2003,(6):903~906
9 祝正威. 雷达信号的周期分选方法. 电子对抗. 1985,11(2):15~22
10 张庆荣 , 单佩均 . 雷达信号脉内特征分析的谱相关法 . 电子对抗 . 1993,19(4):1~6
11 J.P.Y.Lee. A multi-channel digital receiver: intra-pulse analysis and direction-finding. Proceedings of IEEE Pacific Rim Conference on Communications Computers and Signal Processing. 1999,589~592
12 Y.F.Zhou, J.P.Y.Lee. A MDL approach for determining the number of emitters using intra-pulse information. Proceedings of IEEE Pacific Rim Conference on Communications ,Computers and Signal Processing.. 1999,548~551
13 黄知涛,周一宇,姜文利. 基于相对无模糊相位重构的自动脉内调制特性分析. 通信学报. 2003,24(4):153~160
14 张葛祥,胡来招,金炜东. 基于熵特征的雷达辐射源信号识别. 电波科学学报. 2005,20(4):440~445
15 陈天璐, 阙沛文. 信息融合多传感器可信度的确定方法及应用. 测试技术学报. 2005,19(1):61~64
16 蓝金辉, 马宝华, 蓝天, 周兆英. 多传感器最大属性数据融合及其目标识别应用. 清华大学学报(自然科学版). 2000,40(4):21~23
17 Shuhong Tang, YiShen, Zhiyan Liu. Data Fusion Using Fuzzy Measures and Genetic Algorithms. IEEE. 2000,1113~1117
18 敬忠良, 杨永胜, 李建勋, 戴冠中. 基于模糊神经网络和 D-S 推理的智能特征信息融合研究. 信息与控制. 1997, 26(2): 107~111
19 杨万海. 多传感器数据融合及其应用. 西安电子科技大学出版社. 2004
20 张乃尧, 阎平凡. 神经网络与模糊控制. 清华大学出版社. 2004
21 孙宝深, 时银水, 朱岩. 基于模糊神经网络的目标识别. 电光与控制. 2005,3(6):50~54
22 曲晓慧,安钢. 数据融合方法技术及展望. 船舶电子工程 2003,(2):2~5
23 陈绍顺, 王君. 基于前向型神经网络的空袭目标类型识别模型. 现代防御技术. 2002,30(2):57~60
24 李方社,王宝树. 神经网络技术在数据融合中的应用. 西安电子科技大学学报. 1998,25(60):790~793
25 Bogdan Gabrys, Andrzej Bargiela. General Fuzzy Min-Max Neural Network for Clustering and Classification. IEEE Trans. On Neural network. 2000,11(3): 769~783
26 Yan-Qing Zhang, Fu-lai Chung. A Fuzzy Neural Tree with Heuristic Backprogapation Learning. IEEE,2002:553~558
27 邹慧, 管振辉. 侦察机识别库的一种设计方案. 船舶电子对抗. 1997,(3): 10~13
28 王平安, 姜宁.专家系统在电子对抗中的应用. 航天电子对抗. 2000,(4): 46~48
29 Motohide Umano, Itsuo Hatono, Hiroyuki Tamura. Fuzzy expert system shells. IEEE. 1994:219~225
30 Hideyuki, Osamu. The Recognition of Facial Expression Based on Fuzzy Expert System. IEEE. 1998: 565~568
31 高尚, 刘铭. 基于 MATLAB 模糊逻辑工具箱的目标类型识别. 航空计算技术. 2002, 32(1): 52~54
32 Christiaan Pemeel, Jean-Michel Renders, Marc Acheroy. Optimization of FuzzySystems Using Genetic Algorithms and Neural Networks. IEEE Trans. On Fuzzy systems. 1995,3(3):300~311
33 Ric C.C.Tsang, Daniel S.Yeung. Optimizing fuzzy knowledge base by genetic algorithms and neural networks. IEEE. 1999: 367~37
34 Fu-Lai Chung, ji-Cheng Duan. On Multistage Fuzzy Neural Network Modeling. IEEE Trans. On Fuzzy systems. 2000,8(2):125~141
35 Shi-Feng Juang, Chin-Teng Lin. An On-Line self-Constructing Neural Fuzzy Inference Network and Its Applications. IEEE Trans. On Fuzzy systems. 1998,6(1):12~32
36 丁建江, 张贤达, 吕金建. 常规雷达飞机回波调制特性的建模. 系统工程与电子技术. 2003,25(11):1407~1410
37 丁建江, 张贤达. 常规雷达 JEM 特征分析与目标分类的研究. 2003, 25(7):956~962
38 丁建江, 张贤达. 防空雷达飞机目标调制周期特征的研究. 电子学报. 2003,6(6):903~906
39 张贤达. 现代信号处理(第二版). 清华大学出版社. 2003:90~96
40 I.I.Jouny, R.L.Moses. The Bispectrum of Complex Signals: Definitions and Properties. IEEE Trans on SP. 1992,40(11):2833~2836
41 丁建江, 张贤达. 复 AR 双谱及在常规雷达目标分类中的应用. 信号处理. 2002,18(6):556~559
42 S.M.Pincus. Approximate entropy: a complexity measure for biological time series data. Proceedings of the 1991 IEEE Seventeenth Annual Northeast Bioengineering Conference. 1991,35~36
43 红波, 唐庆玉, 杨福生. 近似熵、互近似熵的性质、快速算法及其在脑电与认知研究中的初步应用. 信号处理. 1999,15(2):100~108
44 杨莘元, 蒲书缙, 马惠珠. 多传感器目标识别的优化融合. 宇航学报. 2005,26(1):47~51
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2 李永田, 刁立峰. 航空电子系统多传感器数据融合的目标属性评估. 电光与控制. 2003,10(3):45~47
3 徐毅, 金德琨, 敬忠良. 数据融合研究的回顾与展望. 信息与控制. 2002,31(3):250~255
4 Mark R.Bell, Robert A.Grubbs. JEM Modeling and Measurement for Radar Target Identification. IEEE Trans. on Aerospace and Electronic Systems. 1993,29(1):73~87
5 K.K.Chan, F.Tremblay, S.David. Physical Optics Analysis of Rotating in a Cylinder. Proceeding of the IEEE. 1995:34~37
6 I.Jouny, E.D.Garber, R.L.Moses. Radar Target Identification Using the Bispectrum:A Comparative Study. IEEE Trans. on Aerospace and Electronic Systems. 1995,31(1):69~78
7 M.R.Raghuveer, C.L.Nikias. A Parametic Approach To Bispetrum Estimation. Proceeding of the IEEE. 1884,38(1):1~4
8 丁建江, 张贤达. 防空雷达飞机目标调制周期特征的研究. 电子学报. 2003,(6):903~906
9 祝正威. 雷达信号的周期分选方法. 电子对抗. 1985,11(2):15~22
10 张庆荣 , 单佩均 . 雷达信号脉内特征分析的谱相关法 . 电子对抗 . 1993,19(4):1~6
11 J.P.Y.Lee. A multi-channel digital receiver: intra-pulse analysis and direction-finding. Proceedings of IEEE Pacific Rim Conference on Communications Computers and Signal Processing. 1999,589~592
12 Y.F.Zhou, J.P.Y.Lee. A MDL approach for determining the number of emitters using intra-pulse information. Proceedings of IEEE Pacific Rim Conference on Communications ,Computers and Signal Processing.. 1999,548~551
13 黄知涛,周一宇,姜文利. 基于相对无模糊相位重构的自动脉内调制特性分析. 通信学报. 2003,24(4):153~160
14 张葛祥,胡来招,金炜东. 基于熵特征的雷达辐射源信号识别. 电波科学学报. 2005,20(4):440~445
15 陈天璐, 阙沛文. 信息融合多传感器可信度的确定方法及应用. 测试技术学报. 2005,19(1):61~64
16 蓝金辉, 马宝华, 蓝天, 周兆英. 多传感器最大属性数据融合及其目标识别应用. 清华大学学报(自然科学版). 2000,40(4):21~23
17 Shuhong Tang, YiShen, Zhiyan Liu. Data Fusion Using Fuzzy Measures and Genetic Algorithms. IEEE. 2000,1113~1117
18 敬忠良, 杨永胜, 李建勋, 戴冠中. 基于模糊神经网络和 D-S 推理的智能特征信息融合研究. 信息与控制. 1997, 26(2): 107~111
19 杨万海. 多传感器数据融合及其应用. 西安电子科技大学出版社. 2004
20 张乃尧, 阎平凡. 神经网络与模糊控制. 清华大学出版社. 2004
21 孙宝深, 时银水, 朱岩. 基于模糊神经网络的目标识别. 电光与控制. 2005,3(6):50~54
22 曲晓慧,安钢. 数据融合方法技术及展望. 船舶电子工程 2003,(2):2~5
23 陈绍顺, 王君. 基于前向型神经网络的空袭目标类型识别模型. 现代防御技术. 2002,30(2):57~60
24 李方社,王宝树. 神经网络技术在数据融合中的应用. 西安电子科技大学学报. 1998,25(60):790~793
25 Bogdan Gabrys, Andrzej Bargiela. General Fuzzy Min-Max Neural Network for Clustering and Classification. IEEE Trans. On Neural network. 2000,11(3): 769~783
26 Yan-Qing Zhang, Fu-lai Chung. A Fuzzy Neural Tree with Heuristic Backprogapation Learning. IEEE,2002:553~558
27 邹慧, 管振辉. 侦察机识别库的一种设计方案. 船舶电子对抗. 1997,(3): 10~13
28 王平安, 姜宁.专家系统在电子对抗中的应用. 航天电子对抗. 2000,(4): 46~48
29 Motohide Umano, Itsuo Hatono, Hiroyuki Tamura. Fuzzy expert system shells. IEEE. 1994:219~225
30 Hideyuki, Osamu. The Recognition of Facial Expression Based on Fuzzy Expert System. IEEE. 1998: 565~568
31 高尚, 刘铭. 基于 MATLAB 模糊逻辑工具箱的目标类型识别. 航空计算技术. 2002, 32(1): 52~54
32 Christiaan Pemeel, Jean-Michel Renders, Marc Acheroy. Optimization of FuzzySystems Using Genetic Algorithms and Neural Networks. IEEE Trans. On Fuzzy systems. 1995,3(3):300~311
33 Ric C.C.Tsang, Daniel S.Yeung. Optimizing fuzzy knowledge base by genetic algorithms and neural networks. IEEE. 1999: 367~37
34 Fu-Lai Chung, ji-Cheng Duan. On Multistage Fuzzy Neural Network Modeling. IEEE Trans. On Fuzzy systems. 2000,8(2):125~141
35 Shi-Feng Juang, Chin-Teng Lin. An On-Line self-Constructing Neural Fuzzy Inference Network and Its Applications. IEEE Trans. On Fuzzy systems. 1998,6(1):12~32
36 丁建江, 张贤达, 吕金建. 常规雷达飞机回波调制特性的建模. 系统工程与电子技术. 2003,25(11):1407~1410
37 丁建江, 张贤达. 常规雷达 JEM 特征分析与目标分类的研究. 2003, 25(7):956~962
38 丁建江, 张贤达. 防空雷达飞机目标调制周期特征的研究. 电子学报. 2003,6(6):903~906
39 张贤达. 现代信号处理(第二版). 清华大学出版社. 2003:90~96
40 I.I.Jouny, R.L.Moses. The Bispectrum of Complex Signals: Definitions and Properties. IEEE Trans on SP. 1992,40(11):2833~2836
41 丁建江, 张贤达. 复 AR 双谱及在常规雷达目标分类中的应用. 信号处理. 2002,18(6):556~559
42 S.M.Pincus. Approximate entropy: a complexity measure for biological time series data. Proceedings of the 1991 IEEE Seventeenth Annual Northeast Bioengineering Conference. 1991,35~36
43 红波, 唐庆玉, 杨福生. 近似熵、互近似熵的性质、快速算法及其在脑电与认知研究中的初步应用. 信号处理. 1999,15(2):100~108
44 杨莘元, 蒲书缙, 马惠珠. 多传感器目标识别的优化融合. 宇航学报. 2005,26(1):47~51
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