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含未知参数的自校正融合Kalman滤波器及其收敛性

DOI: 10.3724/SP.J.1004.2012.00109, PP. 109-119

Keywords: 多传感器信息融合,自校正融合,偏差补偿最小二乘法,收敛性,动态误差系统分析方法,Kalman滤波器

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Abstract:

?对于带未知模型参数和噪声方差的多传感器系统,基于分量按标量加权最优融合准则,提出了自校正解耦融合Kalman滤波器,并应用动态误差系统分析(Dynamicerrorsystemanalysis,DESA)方法证明了它的收敛性.作为在信号处理中的应用,对带有色和白色观测噪声的多传感器多维自回归(Autoregressive,AR)信号,分别提出了AR信号模型参数估计的多维和多重偏差补偿递推最小二乘(Biascompensatedrecursiveleast-squares,BCRLS)算法,证明了两种算法的等价性,并且用DESA方法证明了它们的收敛性.在此基础上提出了AR信号的自校正融合Kalman滤波器,它具有渐近最优性.仿真例子说明了其有效性.

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