基于现代时间序列分析方法的多传感器观测融合Kalman滤波器与Wiener滤波器

【作者】 白敬刚；

【导师】 邓自立；

【作者基本信息】 黑龙江大学 ， 控制理论与控制工程， 2005， 硕士

【摘要】 在线性最小方差最优信息融合准则下,对多传感器系统用现代时间序列分析方法,基于ARMA新息模型,应用加权观测融合方法与集中观测融合方法的完全功能等价性,提出了多传感器加权观测融合Kalman滤波器和wiener状态滤波器,提出了多传感器加权观测融合单通道信号wiener估值器,提出了多传感器加权观测融合单通道白噪声wiener反卷积估值器和多传感器加权观测融合单通道信号wiener反卷积滤波器。在各传感器具有相同的观测阵的前提下,加权观测融合方法同集中式观测融合方法相比,不仅可获得全局最优融合估计,而且不增加观测向量的维数,可明显减小计算负担,便于实时应用。大量的仿真例子说明了新提出的结果的有效性。更多还原

【Abstract】 Under the linear minimum variance optimal information fusion criterion,by using the modern time series analysis method,based on the autoregressive moving average (ARMA) innovation model, by using the functional equivalence of the weighted measurement fusion and centralized measurement fusion methods, the multisensor weighted measurement fusion Kalman filter and Wiener state filter, the multisensor weighted measurement fusion single channel Wiener signal filter , the multisensor weighted measurement fusion signle channel white noise Wiener deconvolution filter and the multisensor weighted measurement fusion single channel Wiener deconvolution filter are presented respectively.Assuming that each sensor has the same measurenment matrix,compared with the centralized measurement fusion method,the weighted measurement fusion method not only it give the globally optimal estimation, but also it can obviously reduce the computational burden, so that it is suitable for real time applications.Many simulation examples show their effectiveness.更多还原