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论文:2013,Vol:31,Issue(1):89-93 |
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引用本文： |
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周勇, 张玉峰, 张超, 张举中. 基于Sage-Husa的线性自适应平方根卡尔曼滤波算法[J]. 西北工业大学 |
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Zhou Yong, Zhang Yufeng, Zhang Chao, Zhang Juzhong. A Novel Algorithm of Linear Adaptive Square-Root Kalman Filtering Based on Sage-Husa[J]. Northwestern polytechnical university |
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| 基于Sage-Husa的线性自适应平方根卡尔曼滤波算法 |
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| 周勇1, 张玉峰2, 张超1, 张举中3 |
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1. 西北工业大学 航空学院, 陕西 西安 710072;
2. 西北工业大学 机电学院, 陕西 西安 710072;
3. 中船重工第 713 研究所, 河南 郑州 450000 |
| 摘要: |
| 针对标准卡尔曼滤波和扩展卡尔曼滤波存在的局限性,结合平方根滤波的思想,对传统Sage-Husa估计器进行改进,提出了一种新的线性自适应平方根卡尔曼滤波(Linear Adaptive Square-RootKalman Filtering,LASRKF)算法。该算法直接对系统状态方差阵和噪声方差阵的平方根进行递推与估算,确保了状态和噪声方差阵的对称性和非负定性;算法还增添了对系统噪声统计特性估计的计算,强化了滤波器的稳定性和自适应能力;与传统Sage-Husa自适应滤波算法相比LASRKF可提高滤波器抗发散的能力。仿真实验表明,LASRKF可有效提高滤波器的精确性、稳定性和自适应能力。 |
| 关键词:
算法
协方差矩阵
预估
卡尔曼滤波
线性系统
稳定性
LASRKF算法
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| A Novel Algorithm of Linear Adaptive Square-Root Kalman Filtering Based on Sage-Husa |
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| Zhou Yong1, Zhang Yufeng2, Zhang Chao1, Zhang Juzhong3 |
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1. College of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. School of Mechatronics, Northwestern Polytechnical University, Xi'an 710072, China;
3. 713th Institute of China Shipbuilding Industry Corporation, Zhengzhou, 450000, China |
| Abstract: |
| Aiming at the flaws of the standard Kalman Filter(KF) and Extended Kalman Filter (EKF), and basedon the square-root filtering algorithm, we modify traditional Sage-Husa adaptive filter and present a novel algorithmof Linear Adaptive Square-Root Kalman Filtering(LASRKF) in this paper.With this new filter, the square root ofsystem state covariance matrix is calculated recursively and the estimation of the square root of the system noise co-variance matrix is obtained straightforwardly.Then the positive semi-definiteness of system state and noise covari-ance matrix are guaranteed; the stability and the adaptability of filter are also enhanced.Compared with the tradi-tional Sage-Husa adaptive filtering algorithm, LASRKF algorithm improves the anti-divergence capability.Simula-tion results show preliminarily that the stability, accuracy and adaptability of the filter are improved greatly. |
| Key words:
algorithms
covariance matrix
estimation
Kalman filters
linear systems
stability;LASRKF(LinearAdaptive Square-Root Kalman Filtering) algorithm
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| 收稿日期: 2012-03-12
修回日期:
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| DOI: |
| 基金项目: 国家自然科学基金(51207129、61104030)资助 |
| 通讯作者:
Email： |
| 作者简介: 周勇(1978-)，西北工业大学讲师，主要从事电气工程及自动化的研究。
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| 作者相关文章 |
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| 周勇 在本刊中的所有文章 |
| 张玉峰 在本刊中的所有文章 |
| 张超 在本刊中的所有文章 |
| 张举中 在本刊中的所有文章 |
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