多传感器最优信息融合白噪声反卷积滤波器

Multisensor Optimal Information Fusion White Noise Deconvolution Filter

基于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,提出了带相关噪声系统多传感器信息融合白噪声反卷积滤波器.提出了各传感器滤波误差之间的协方差阵计算公式,可用于计算最优融合加权阵.同单传感器情形相比,可提高融合滤波精度.它可减少在线计算负担,便于实时应用.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.

Based on the Kalman filtering method and white noise estimation theory, under linear minimum variance information fusion criterion weighted by matrices, a multisensor information fusion white noise deconvolution filter is presented for systems with correlated noises. The formula of computing covariances among filtering errors of sensors is presented, which can be applied to compute the optimal fused weighting matrices. Compared to the single sensor case, the accuracy of fused filtering is improved. It can reduce the on-line computational burden, and is suitable for real time applications. It can be applied to signal processing in oil seismic exploration. A simulation example for 3-sensor information fusion Bernoulli-Gaussian white noise deconvolution filter shows its effectiveness.