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线性/非线性系统混合滤波 被引量：1

Mixed Filtering of Linear/Nonlinear System
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摘要 针对采用混合坐标系的跟踪系统,提出线性/非线性系统混合滤波方法(SRUKF-KF算法)。该方法用SR-UKF估计系统状态空间模型中的非线性部分,用SR-KF估计其线性部分,提高了线性部分估计的精度和运算效率。仿真结果表明,该方法具有较强数值稳定性,较高估计精度和较好的实时性。 A mixed filtering method of linear/nonlinear system （SRUKF-KF algorithm） is presented in terms of tracking system with mixed coordinate system. In the method, the nonlinear parts of a system model are estimated with SR-UKF while its linear parts are estimated with the SR-KF, which enhances the accuracy and the computation efficiency of linear parts estimation. The simulation results show that the method is of stronger numerical stability, higher accuracy and real-time quality.
作者 叶继坤 卓启明 雷虎民 郝利超
机构地区 空军工程大学导弹学院 中国人民解放军驻一四九厂军代表室
出处 《现代防御技术》 北大核心 2010年第3期104-107,共4页 Modern Defence Technology
基金 航空基金(20090196005)
关键词 线性/非线性系统 混合滤波 SRUKF—KF 非线性滤波 目标跟踪 linear/nonlinear system mixed filtering square-root unscented Kalman filter and Kalman filter（SRUKF-KF） nonlinear filtering target tracking
分类号 TN713 [电子电信—电路与系统]
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参考文献6

  • 1RUDOLPH V M. Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models [ C ]. In Workshop on Advances in Machine Learning Montreal. June 2003.
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  • 6尹建君,张建秋,林青.Unscented卡尔曼滤波-卡尔曼滤波算法[J].系统工程与电子技术,2008,30(4):617-620. 被引量：19

二级参考文献10

  • 1Doucet A, Godsill S, Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering[J]. Statistics and Computing, 2000 (10) : 197 - 208.
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现代防御技术
2010年 第3期

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