[1]程力,蔡体菁.基于模式识别神经网络的重力匹配算法[J].东南大学学报(自然科学版),2007,37(5):839-843.[doi:10.3969/j.issn.1001-0505.2007.05.020]
 Cheng Li,Cai Tijing.Gravity matching algorithm based on pattern recognition neural network[J].Journal of Southeast University (Natural Science Edition),2007,37(5):839-843.[doi:10.3969/j.issn.1001-0505.2007.05.020]
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基于模式识别神经网络的重力匹配算法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
37
期数:
2007年第5期
页码:
839-843
栏目:
仪器科学与技术
出版日期:
2007-09-20

文章信息/Info

Title:
Gravity matching algorithm based on pattern recognition neural network
作者:
程力1 蔡体菁2
1 东南大学能源与环境学院, 南京 210096; 2 东南大学仪器科学与工程学院, 南京 210096
Author(s):
Cheng Li1 Cai Tijing2
1 School of Energy and Environment, Southeast University, Nanjing 210096,China
2 School of Instrument Science and Engineering, Southeast University, Nanjing 210096,China
关键词:
组合导航系统 重力匹配 模式识别 概率神经网络
Keywords:
integrated navigation system gravity matching pattern recognition probabilistic neural network
分类号:
U666.1
DOI:
10.3969/j.issn.1001-0505.2007.05.020
摘要:
为了提高重力辅助惯性导航系统在重力异常明显区域内的定位精度和匹配率,用模式识别神经网络的方法进行了重力匹配.在匹配时刻,根据惯导指示位置确定在一定的网格点范围内搜索载体真实位置,以每个网格点为终点把惯导指示航迹放置到重力图上,由此提取一系列的参考重力图上数据,并把它和对应网格点的位置定义成一个模式类,把所有的模式类作为概率神经网络的样本训练一个模式识别神经网络,然后把重力仪测量数据使用该神经网络识别到某个模式类,对比模式类的定义可以确定此时的载体位置.计算仿真研究表明,该算法的重力匹配率优于通常的相关匹配算法,其组合导航系统的定位误差在1个重力图网格左右.
Abstract:
In order to improve the locating precision and matching rate of the gravity aided inertial navigation system(INS)in regions with significant gravity anomaly characteristic, the pattern recognition neural network was used to investigate the problem of gravity matching. While matching, a scope with certain grid points surrounding the INS indicating position was plotted as the matching area to search the real location of the vehicle. A set of gravity data was then drawn from reference gravity map by placing the indicating track of the INS onto the gravity map. These data were defined as some pattern classes and used to train a probabilistic neural network. The measurements of gravity meter can be recognized to one pattern class by this neural network and the vehicle can be located according to the definition of this pattern class. Simulation results show that the presented algorithm achieves better performance than ordinary correlative matching algorithm and the matching precision is approximately one grid on gravity map.

参考文献/References:

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备注/Memo

备注/Memo:
基金项目: 国家高技术研究发展计划(863计划)资助项目(2006AA12Z302).
作者简介: 程力(1971—),男,博士生,讲师; 蔡体菁(联系人),男,博士,教授,博士生导师, caitij @seu.edu.cn.
更新日期/Last Update: 2007-09-20