| 引用本文: | 沈凯,聂吾希斌 K.A.,刘荣忠,等.基于非线性Kalman滤波的导航系统误差补偿技术.[J].国防科技大学学报,2017,39(2):84-90.[点击复制] |
| SHEN Kai,Neusypin K.A.,LIU Rongzhong,et al.Technology of error compensation in navigation systems based on nonlinear Kalman filter[J].Journal of National University of Defense Technology,2017,39(2):84-90[点击复制] |
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| 基于非线性Kalman滤波的导航系统误差补偿技术 |
| 沈凯1,2, 聂吾希斌 K.A.3, 刘荣忠1, 普拉列达尔斯基 A.V.3, 郭锐1 |
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(1. 南京理工大学 机械工程学院， 江苏 南京 210094;2.
2. 莫斯科鲍曼国立技术大学 计算机科学与控制系统学院， 俄罗斯 莫斯科 105005;3.2. 莫斯科鲍曼国立技术大学 计算机科学与控制系统学院， 俄罗斯 莫斯科 105005)
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| 摘要: |
| 针对非线性非高斯导航系统信息处理问题，采用自组织算法、神经网络和遗传算法等改进传统非线性Kalman滤波算法，构建一种自适应的组合导航系统。应用具有冗余趋势项的自组织算法、Volterra神经网络和遗传算法，建立导航系统误差的非线性预测模型，进而计算得到其预测值；将该预测值与Kalman滤波算法求得的估计值进行比较得到差值，以此监测Kalman滤波算法的工作状态；采用自适应控制方法，在导航系统结构层面改进Kalman滤波算法，构建新型的导航系统误差补偿模型。开展基于导航系统KIND-34的半实物仿真研究，应用所提出的改进方法改善了导航系统误差的补偿效果，提高了组合导航系统的自适应能力和容错能力。 |
| 关键词: 组合导航系统 导航系统误差补偿 非线性Kalman滤波 自组织算法 遗传算法 |
| DOI：10.11887/j.cn.201702012 |
| 投稿日期：2015-09-19 |
| 基金项目:国家自然科学基金青年科学基金资助项目（11102088）；高等学校学科创新引智计划资助项目（B16025） |
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| Technology of error compensation in navigation systems based on nonlinear Kalman filter |
| SHEN Kai1,2, Neusypin K.A.3, LIU Rongzhong1, Proletarsky A.V.3, GUO Rui1 |
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(1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;2.
2. Faculty of Computer Science and Control Systems, Bauman Moscow State Technical University, Moscow 105005, Russia;3.2. Faculty of Computer Science and Control Systems, Bauman Moscow State Technical University, Moscow 105005, Russia)
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| Abstract: |
| As for nonlinear/non-Gaussian information processing problems in navigation systems, a kind of adaptive integrated navigation system was established on the basis of the modified traditional nonlinear Kalman filter by utilizing self-organization algorithm, neural network and genetic algorithm. Applying self-organization algorithm with redundant trends, Volterra neural network and genetic algorithm, the nonlinear prediction model of navigation system error was built. Then, predicted values of navigation errors were obtained using the established error model. Comparing the predicted values with the estimated values by Kalman filtering algorithm, the difference between them, functioning as an indicator of the divergence of Kalman filter, was formulated. The modification of nonlinear Kalman filter was made and a novel technology of navigation error compensation was thus developed on the basis of adaptive control methods. Applying traditional and modified Kalman filtering algorithms respectively, the semi-physical simulation study based on the navigation system KIND-34 was carried out. The analyzed results indicate that the accuracy of error estimation and compensation in navigation systems is improved by using the modified nonlinear Kalman filter, and thus the ability of self-adaption and fault tolerance are enhanced in integrated navigation systems. |
| Keywords: integrated navigation system navigation error compensation nonlinear Kalman filter self-organization algorithm genetic algorithm |
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