基于Kalman滤波方法的多传感器信息融合最优白噪声反卷积滤波器  

Multi-sensor information fusion optimal white noise de-convolution filter based on Kalman filtering method

作　　者：王欣[1] 李云[1] 邓自立[1] 

机构地区：[1]黑龙江大学电子工程学院,黑龙江哈尔滨150080

出　　处：《黑龙江大学自然科学学报》2006年第2期227-230,234,共5页Journal of Natural Science of Heilongjiang University

基　　金：国家自然科学基金资助项目(60374026);黑龙江大学青年科学基金资助项目(QL200414)

摘　　要：应用Kalman滤波方法和白噪声估计理论,在线性最小方差按矩阵加权最优信息融合准则下,提出了多传感器信息融合稳态最优白噪声反卷积滤波器,其中给出了局部滤波误差之间的协方差公式,它被用于计算最优融合加权阵.同单传感器情形相比,可提高滤波精度.它可应用于石油地震勘探信号处理.一个3传感器信息融合Bernoulli-Gaussian白噪声反卷积滤波器的仿真例子说明了其有效性.Using the Kalman filter method and white noise estimation theory, under the linear minimum variance optimal information fusion criterion, the multi - sensor information fusion steady - state optimal white noise de - convolution filter is presented, where the formula for computing covariance matrices between the local filtering error is given, which can be applied to compute the optical fusion weighting coefficient matrices. Compared with the single sensor case, the accuracy of the fused filter is improved. It can be applied to signal processing in oil seismic exploration. A simulation example for three sensors information fusion Bernoulli - Gaussian white noise de - convolution filter shows their effectiveness.

关 键 词：最优信息融合 反射地震学 反卷积 白噪声估值器 Kalman滤波方法 

分 类 号：O211.64[理学—概率论与数理统计]