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加权融合鲁棒增量Kalman滤波器

孙小君 周晗 沈海滨 闫广明

孙小君, 周晗, 沈海滨, 闫广明. 加权融合鲁棒增量Kalman滤波器[J]. 电子与信息学报. doi: 10.11999/JEIT200122
引用本文: 孙小君, 周晗, 沈海滨, 闫广明. 加权融合鲁棒增量Kalman滤波器[J]. 电子与信息学报. doi: 10.11999/JEIT200122
Xiaojun SUN, Han ZHOU, Haibin SHEN, Guangming YAN. Weighted Fusion Robust Incremental Kalman Filter[J]. Journal of Electronics and Information Technology. doi: 10.11999/JEIT200122
Citation: Xiaojun SUN, Han ZHOU, Haibin SHEN, Guangming YAN. Weighted Fusion Robust Incremental Kalman Filter[J]. Journal of Electronics and Information Technology. doi: 10.11999/JEIT200122

加权融合鲁棒增量Kalman滤波器

doi: 10.11999/JEIT200122
基金项目: 国家自然科学基金(61104209),黑龙江省高校基本科研业务费黑龙江大学专项资金(2020-KYYWF-0998)
详细信息
    作者简介:

    孙小君:女,1980年生,副教授,研究方向为多传感器信息融合、状态估计、信号处理

    周晗:男,1993年生,硕士生,研究方向为多传感器信息融合、系统辨识

    沈海滨:男,1994年生,硕士生,研究方向为多传感器信息融合、系统辨识

    闫广明:男,1979年生,讲师,研究方向为多传感器信息融合、状态估计

    通讯作者:

    周晗 1120546259@qq.com

  • 中图分类号: TN713, TP18

Weighted Fusion Robust Incremental Kalman Filter

Funds: The National Natural Science Foundation of China (61104209), The Special Funds of Heilongjiang University of Basic Scientific Research Expenses for Colleges and Universities in Heilongjiang Province (2020-KYYWF-0998)
  • 摘要: 在一定环境条件下,当系统的量测方程没有进行验证或校准时,使用该量测方程往往会产生未知的系统误差,从而导致较大的滤波误差。同样的,当系统的噪声方差不确定时,滤波的性能也将会变坏,甚至会引起滤波器发散。增量方程的引入可以有效消除系统的未知量测误差,从而带未知量测误差的欠观测系统的状态估计问题可以转换为增量系统的状态估计问题。该文考虑带未知量测误差和未知噪声方差的线性离散系统,首先提出一种基于增量方程的鲁棒增量Kalman滤波器。进而,基于线性最小方差最优融合准则,提出一种加权融合鲁棒增量Kalman滤波算法。仿真实例证明了所提算法的有效性和可行性。
  • 图  1  局部传感器1的经典滤波器、精确增量滤波器和鲁棒增量滤波器均方误差比较

    图  2  局部传感器2的经典滤波器、精确增量滤波器和鲁棒增量滤波器均方误差比较

    图  3  局部传感器3的经典滤波器、精确增量滤波器和鲁棒增量滤波器均方误差比较

    图  4  局部和加权融合鲁棒增量Kalman滤波器的均方误差比较

    表  1  局部和加权融合鲁棒增量Kalman滤波器在时刻k=200时的均方误差值比较

    传感器1传感器2传感器3融合器
    0.25930.26100.25970.2490
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-02-21
  • 修回日期:  2021-03-07
  • 网络出版日期:  2021-03-29

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