多传感器分布式信息融合Wiener状态估值器

Multisensor Distributed Information Fusion Wiener State Estimators

应用现代时间序列分析方法,基于ARMA新息模型、白噪声估值器和观测预报器,在按矩阵加权、按 标量加权和按对角阵加权的线性最小方差最优信息融合规则下,提出了相应的三种最优分布式融合Wiener 状态估值器,可统一处理融合滤波、平滑和预报问题。为了计算最优加权,提出了状态估计误差方差阵和互 协方差阵的计算公式。同单传感器情形相比,可提高滤波精度。一个带四传感器目标跟踪系统的仿真例子 说明了其有效性和正确性,并说明了三种加权融合估计精度无显著差异,因而采用按标量加权融合器可显著 减小计算负担,便于实时应用。

Using the modern time series analysis method, based on the autoregressive moving average （ARMA） innovation model, white noise estimator and measurement predictor, under the linear minimum variance optimal fusion rules weighted by matrices, scalars and diagonal matrices, the corresponding three multisensor distributed fusion Wiener state estimators are presented. They can handle the fused filtering, smoothing and prediction problems in a unified framework. The formulas of computing the variance and cross-covariance matrices among local state estimation errors are presented, which are applied to compute the optimal weights. Compared with the single sensor case, the accuracy of the fused estimation is improved. A simulation example for the target tracking system with four-sensor shows they effectiveness and correctness,and shows the accuracy distinction of three fused estimators is not obvious, so that employing the fused filter weighted by scalars can obviously reduce the computational burden, and it is suitable for real time applications.