基于Kalman滤波方法的多传感器观测融合滤波器

【作者】 崔崇信；

【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】 对于多传感器系统,在线性最小方差最优融合估计准则下,应用经典Kalman滤波方法,基于Riccati方程,在假设各传感器具有相同观测阵条件下,本文证明了集中观测融合方法和加权观测融合方法是完全功能等价的。在此基础上提出多传感器加权观测融合Kalman估值器(滤波器、预报器和平滑器)和Wiener状态估值器,提出加权观测融合Wiener信号估值器,提出加权观测融合白噪声Wiener反卷积估值器,且提出了加权观测融合Wiener信号反卷积估值器。同集中观测融合方法相比,加权观测融合方法不仅可获得全局最优估计,而且明显减小计算负担,便于实时应用。大量的仿真例子说明了算法的有效性。更多还原

【Abstract】 Under the linear minimum variance optimal fused estimation criterion, by applying the Kalman filtering method, based on the Riccati equation, assuming that each sensor has the same measurement matrix, the completely functional equivalent of the centralized measurement method and the weighted measurement fusion method is proved. Based on this, the multisen-or weighting measurement fusion Kalman estimators(filter, predictor, smoother) and Wiener state estimators are presented, the weighting measurement fusion Wiener signal estimators, the weighting measurement fusion white noise deconvolution estimators and the weighting measurement fusion Wiener signal deconvolution estimators are also presented. Compared with the centralized measurement fusion method, by the weighted measurement fusion method, not only the globally optimal estimation can be obtained, but also the computational burden can obviously be reduced and it is suitable for real time applications. Many simulation examples show their effectiveness.更多还原