| 基于进化人工神经网络的组合系统滤波技术 |
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| 引用本文： | 徐晓苏,刘建娟. 基于进化人工神经网络的组合系统滤波技术[J]. 中国惯性技术学报, 2008, 16(1): 82-86 |
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| 作者姓名： | 徐晓苏 刘建娟 |
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| 作者单位： | 1. 东南大学,南京,210096
2. 河南工业大学,郑州,450007 |
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| 基金项目： | 国防自然科学基金 , 国家重点基础研究发展计划(973计划) , 高等学校博士学科点专项科研项目 |
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| 摘 要： | 在组合系统运用Kalman滤波器技术时,准确的系统模型和可靠的观测数据是保证其性能的重要因素,否则将大大降低Kalman滤波器的估计精度,甚至导致滤波器发散.为解决上述Kalman应用中的实际问题,提出了一种新颖的基于进化人工神经网络技术的自适应Kalman滤波器.仿真试验表明该算法可以在系统模型不准确时、甚至外部观测数据短暂中断时,仍能保证Kalman滤波器的性能.
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| 关 键 词： | 捷联惯性导航系统 自适应滤波 进化算法 人工神经网络 SINS adaptive filter evolutionary programming (EP) artificial neural networks (ANN) 进化人工神经网络 组合系统 滤波技术 artificial neural networks evolutionary based algorithms Kalman filter integrated navigation system results traditional better accuracy simulations adaptive used lead divergence cause precision |
| 文章编号： | 1005-6734(2008)01-0082-04 |
| 修稿时间： | 2007-11-20 |
Integrated navigation system filter algorithms based on evolutionary artificial neural networks |
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| XU Xiao-su,LIU Jian-juan. Integrated navigation system filter algorithms based on evolutionary artificial neural networks[J]. Journal of Chinese Inertial Technology, 2008, 16(1): 82-86 |
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| Authors: | XU Xiao-su LIU Jian-juan |
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| Abstract: | The performance of Kalman filter in integrated navigation system depends on accurate system model and reliable observation data. Inaccurate system model or trustless observation data will cause low precision of Kalman filter, and even lead filter to divergence. So a new adaptive Kalman filter based on evolutionary artificial neural networks is used in this system. The algorithm is tested by simulations, and the results indicate that the proposed algorithm can efficiently overcome the shortcomings of traditional Kalman filter with better accuracy. |
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| Keywords: | SINS adaptive filter evolutionary programming (EP) artificial neural networks (ANN) |
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