| 引用本文: | 于旭东,魏学通,李莹,等.RBF神经网络在单轴旋转惯导系统轴向陀螺漂移辨识中的应用.[J].国防科技大学学报,2012,34(3):48-52.[点击复制] |
| YU Xudong,WEI Xuetong,LI Ying,et al.Application of Radial Basis Function Network for Identification of Axial RLG Drifts in Single-axis Rotation Inertial Navigation System[J].Journal of National University of Defense Technology,2012,34(3):48-52[点击复制] |
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| RBF神经网络在单轴旋转惯导系统轴向陀螺漂移辨识中的应用 |
| 于旭东1, 魏学通2, 李莹2, 龙兴武1 |
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(1.国防科技大学 光电科学与工程学院，湖南 长沙 410073;2.中国人民解放军92941部队95分队，辽宁 葫芦岛 125000)
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| 摘要: |
| 在激光陀螺单轴旋转惯性导航系统中，单轴旋转可以自动补偿垂直于旋转轴上的惯性器件误差，却不能消除旋转轴方向上惯性器件的误差，因此单轴旋转惯性导航系统的导航精度主要由轴向陀螺漂移决定。提出了一种基于径向基函数神经网络的轴向陀螺漂移辨识方法，利用系统纬度误差和温度变化量作为训练集，针对系统热态、冷态两种情况对RBF神经网络进行训练，对轴向陀螺漂移的辨识精度达到0.0003°/h。试验结果表明：该方法能够有效地辨识轴向陀螺漂移，使系统达到较高的导航精度，满足实际应用的需要。 |
| 关键词: 激光陀螺 惯导系统 单轴旋转 陀螺漂移 RBF神经网络 |
| DOI： |
| 投稿日期：2011-09-20 |
| 基金项目: |
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| Application of Radial Basis Function Network for Identification of Axial RLG Drifts in Single-axis Rotation Inertial Navigation System |
| YU Xudong1, WEI Xuetong2, LI Ying2, LONG Xingwu1 |
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(1.College of Opto electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China;2.Element 95 Unit 92941 of PLA, Huludao 125000, China)
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| Abstract: |
| In the single-axis rotation inertial navigation system with ring laser gyroscope (RLG), the single axis rotation can compensate the vertical errors of the inertial apparatus automatically, but cannot compensate the axial vertical errors, so the precision of the system is determined by the drift of the axial RLG. A novel identification method based on radial basis function network is proposed for the axial RLG drift. The inputs of the network are the latitude error and change of the temperature, and the network is trained for steady and non steady state, in which the identification capability is less than 0.0003°/h. The experiments show that this method can estimate the axial RLG drift efficaciously, and the result of the navigation is excellent and can meet the practical demand. |
| Keywords: ring laser gyroscope inertial navigation system single axis rotation gyro drift RBF neural networks |
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