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带未知观测丢失率的自校正加权观测融合估计 预览 被引量:3

Self-tuning weighted measurement fusion estimation with unknown missing measurement rate
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摘要 对于带未知丢失观测率的离散线性随机系统,应用伯努利随机变量来描述观测丢失现象。采用相关函数法辨识丢失观测率。应用加权最小二乘法(WLS)把高维的观测向量进行压缩得到加权观测融合方程。将实时辨识的观测丢失率代入最优加权观测融合滤波器中得到自校正加权观测融合滤波算法。所获得的自校正加权观测融合滤波器收敛于最优融合滤波器。仿真例子验证了算法的有效性。 For discrete-time linear stochastic systems with unknown missing measurement rates, the Bernoulli random variables are used to describe the phenomena of missing measurement and the correlation functions are used to identify the missing measurement rates. The weighted least squares (WLS) method is used to compress the high- dimensional measurement vector to obtain weighted measurement fusion equation. A self-tuning weighted measurement fusion filtering algorithm is obtained by substituting the real-time identified missing measurement rates into the optimal weighted measurement fusion filter. Moreover, the proposed self-tuning weighted measurement fusion filter converges to the optimal fusion filter. A simulation example verifies the effectiveness of the proposed algorithm.
作者 史腾飞 段广全 孙书利 SHI Teng-Fei, DUAN Guang-Quan, SUN Shu-Li ( School of Electronic Engineering, HeilongiiangUniversity, Harbin 150080, China)
出处 《黑龙江大学工程学报》 2017年第3期71-75,共5页 Journal of Heilongjiang Hydraulic Engineering College
基金 国家自然科学基金资助项目(61573132)
关键词 丢失观测率 自校正 KALMAN滤波器 加权观测融合 missing measurement rate self-tuning Kalman filter weighted measurement fusion
作者简介 史腾飞(1990.),男,河南郑州人,硕士研究生,研究方向:状态估计,E-mail:tengfeidashi@163.com; 通讯作者:孙书利(1971-),男,教授,博士,博士研究生导师,研究方向:信息融合、状态估计,E-mail:sunsl@hlju.edu.cn。
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