基于MIMOFNN模型的弹道导弹目标
时空序贯融合识别方法

doi:10.16183/j.cnki.jsjtu.2017.09.018

摘要/Abstract

摘要： 针对传统弹道导弹(BM)目标时空序贯融合识别算法识别效率低、抗噪性能差的缺点，提出了一种基于多输入多输出模糊神经网络(MIMOFNN)模型的BM目标时空序贯融合识别方法.该方法首先根据多层融合的思想，结合神经网络和模糊理论，提出了多传感器多特征MIMOFNN模型；其次，在此基础上，将当前时刻的融合结果与下一时刻的融合结果再融合，得到此时刻时空序贯融合识别结果，并将其与识别门限比较，直到满足识别门限要求，时空序贯融合识别结束，并做出决策；最后通过实验验证了所提模型的有效性和良好的抗噪性.

关键词: , 多输入多输出, 模糊神经网络, 弹道导弹, 时空序贯融合, 目标识别

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

中图分类号:

TN 95

李昌玺1,2,周焰1,林菡3,李灵芝1,郭戈1. 基于MIMOFNN模型的弹道导弹目标
时空序贯融合识别方法[J]. 上海交通大学学报（自然版）, 2017, 51(9): 1138-.

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-.

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