稳态Kalman滤波增益估计的两种新算法及其应用

TWO NEW ALGORITHMS FOR ESTIMATION OF STEADY-STATE KALMAN FILTER GAIN AND THEIR APPLICATIONS

  • 摘要: 本文从时间序列分析观点(1),基于观测过程的CARMA新息模型,提出了稳态Kalman滤波增益估计的两种新算法及相应的自校正Kalman滤波器,形成一种新的自适应Kalman滤波技术.新算法比Mehra(3)和Tajima(4)的算法简单.作为应用例子,对于一个简单的跟踪系统,导出了带输入估计的自校正α-β滤波器,仿真结果说明了新算法的有效性.

     

    Abstract: From the point of view of time series analysis(1),based on CARMA innovation model ofmeasurement process,this paper presents two new algorithms for estimating the steady-state Kalman filtergain,and the corresponding self-tuning Kalman filters,which form a new adaptive Kalman filtering technique.New algorithms are simpler than that of Mehra(3) and Tajima(4).As an application example,self-tuning α-β tracking filter with input estimation is given,and simulation results show the effectivenessof the new algorithms.

     

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