| 引用本文: | 马剑飞,颜冰,林春生,等.基于多通道卷积神经网络的磁性舰船目标运动参数估计.[J].国防科技大学学报,2020,42(4):78-84.[点击复制] |
| MA Jianfei,YAN Bing,LIN Chunsheng,et al.Motion parameter estimation of magnetic ship target based on multi-channel convolutional neural network[J].Journal of National University of Defense Technology,2020,42(4):78-84[点击复制] |
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| 基于多通道卷积神经网络的磁性舰船目标运动参数估计 |
| 马剑飞,颜冰,林春生,陈浩 |
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(海军工程大学 兵器工程学院, 湖北 武汉 430033)
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| 摘要: |
| 传统磁性目标运动估计效果依赖于目标的初始状态信息,为克服这一缺陷,建立磁性运动目标三分量投影模型,并据此生成磁性舰船运动目标在运动速度、航向、信噪比等参数变化情况下的10类目标的训练数据集、验证数据集以及测试数据集。设计多通道卷积神经网络,对目标的正横距离和运动速度进行估计,并比较和分析了不同的学习方式和激活函数对网络性能的影响。结果表明:Adam+tanh组合方式的估计性能要优于其他组合方式,而且对磁性目标运动参数的估计效果比较精确,此方法相较于卡尔曼滤波、粒子滤波等估计算法的优越性在于运算复杂度低以及参数估计不需要目标初始状态信息。 |
| 关键词: 磁性目标 投影模型 多通道卷积神经网络 参数估计 |
| DOI：10.11887/j.cn.202004013 |
| 投稿日期：2019-01-03 |
| 基金项目:国家自然科学基金资助项目(51509252) |
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| Motion parameter estimation of magnetic ship target based on multi-channel convolutional neural network |
| MA Jianfei, YAN Bing, LIN Chunsheng, CHEN Hao |
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(College of Weaponry Engineering, Naval University of Engineering, Wuhan 430033, China)
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| Abstract: |
| In order to overcome this defect of traditional magnetic target motion estimation′s dependence on the initial state information of the target, a three-axis projection model of magnetic moving ship targets was established, and 10 kinds of target training datasets, validation data sets and test data sets of magnetic ship moving targets with variable parameters were generated. Multi-channel convolutional neural network was designed to estimate the distance abeam and velocity of the target, and the effects of different learning methods and activation functions on the performance of the network were compared and analyzed. The results show that the performance of Adam+tanh method is better than other methods, and the estimation effect of motion parameters is accurate. Compared with Kalman filter and particle filter, this estimation algorithm is calculated with preferable efficiency and independent of initialization for estimation. |
| Keywords: magnetic target projection model multi-channel convolutional neural network parameter estimation |
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