Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2017 , Vol. 51 >Issue 9: 1138

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2017.09.018

 TemporalSpatial Sequential Fusion Recognition Method of
 Ballistic Missile Target Based on MIMOFNN Model

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  •  1. Air Force Early Warning Academy, Wuhan 430019, China;
    2. Unit No．66132 of PLA, Beijing 100043, China; 3. Department of Computer Science,
     Dongfang College, Fujian Agriculture and Forestry University, Fuzhou 350017, China

Online published: 2017-09-20

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Abstract

 In traditional temporalspatial sequential fusion recognition method of ballistic missile (BM) target, there always exits the problem that the efficiency is low and the antinoise performance is bad. In order to solve these difficulties, this paper proposed a fusion recognition method which is called temporalspatial sequential fusion recognition method of BM target based on multiple input multiple output and fuzzy neural network (MIMOFNN) model. In this model, firstly, we use the idea of multi layer fusion, combine with neural network and fuzzy theory, and put forward the MIMOFNN model with multi sensors of multi features. And then, we reintegrate the results of present moment and next moment to get the fusion results. Meanwhile, we compare with recognition threshold, until the fusion results match the recognition threshold, and the process of fusion ends. Finally, the experiment validates the effectiveness and antinoise performance of this model.

Key words：  ; multiple input multiple output (MIMO); fuzzy neural network (FNN); ballistic missile (BM); temporalspatial sequential fusion; target recognition

Cite this article

LI Changxi1,2,ZHOU Yan1,LIN Han3,LI Lingzhi1,GUO Ge1 .  TemporalSpatial Sequential Fusion Recognition Method of
 Ballistic Missile Target Based on MIMOFNN Model[J]. Journal of Shanghai Jiaotong University, 2017 , 51(9) : 1138 . DOI: 10.16183/j.cnki.jsjtu.2017.09.018

References

 ［1］周万幸. 弹道导弹雷达目标识别技术［M］. 北京：电子工业出版社,2011.
［2］吴瑕, 周焰, 杨龙坡,等. 基于聚类分析与几何的目标特征敏感性评估算法［J］. 控制与决策, 2012, 27(6): 914918.
WU Xia, ZHOU Yan, YANG Longpo, et al. Target feature sensitivity evaluation method based on clustering analysis and geometry［J］. Control and Decision, 2012, 27(6): 914918.
［3］吴瑕,周焰,蔡益朝,等.多传感器目标融合识别系统模型研究现状与问题［J］.字航学报, 2010, 31(5): 14131420.
WU Xia, ZHOU Yan, CAI Yichao, et al. Research actualities and problems on multisensor target recognition system model［J］. Journal of Astronautics，2010, 31(5): 14131420.
［4］焦朋勃, 黄长强, 蔡佳, 等. 基于时空信息融合的复合引信目标识别方法［J］. 探测与控制学报, 2014, 36(5): 4750.
JIAO Pengbo, HUANG Changqiang, CAI Jia, et al. Target recognition of compound fuse based on temporalspatial information fusion［J］. Journal of Detection & Control, 2014, 36(5): 4750.
［5］洪昭艺, 高勋章, 黎湘. 基于DS理论的混合式时空域信息融合模型［J］.信号处理, 2011, 27(1): 1419.
HONG Zhaoyi, GAO Xunzhang, LI Xiang. Research on temporalspatial information fusion model based on DS theory［J］. Signal Processing, 2011, 27(1): 1419.
［6］王宇晨, 胡晓伟, 李伟龙. 基于时空序贯融合的TBM拦截效果评估［J］. 火力与指挥控制, 2015, 40(2): 100104.
WANG Yuchen, HU Xiaowei, LI Weilong. Intercepting effect evaluation of TBM based on temporalspatial information fusion［J］. Fire Control & Command Control, 2015, 40(2): 100104.
［7］吴俊, 程咏梅, 曲圣杰, 等. 基于三级信息融合结构的多平台多雷达目标识别算法［J］. 西北工业大学学报, 2012, 30(3): 367372.
WU Jun, CHENG Yongmei, QU Shengjie, et al. An effective multiplatform multiradar target identification algorithm based on three level fusion hierarchical structures［J］. Journal of Northwestern Polytechnical University, 2012, 30(3): 367372.
［8］朱玉鹏, 付耀文, 黎湘,等. 决策层时空信息融合的神经网络模型研究［J］.系统工程与电子技术, 2008, 30(6): 10981102.
ZHU Yupeng, FU Yaowen, LI Xiang, et al. Research on a new network model for temporalspatial information fusion at decision level［J］. Systems Engineering and Electronics, 2008, 30(6): 10981102.
［9］罗大庸, 张远. 多传感器信息时空融合模型及算法研究［J］.系统工程与电子技术, 2004, 26(1): 3639.
LUO Dayong, ZHANG Yuan. Research of spatialtemporal architecture model and the algorithm for multisensor information fusion［J］. Systems Engineering and Electronics, 2004, 26(1): 3639.
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