自校正解耦信息融合Wiener状态估值器

Self-tuning decoupled information fusionWiener state estimators

中文关键词  多传感器信息融合  解耦融合  辨识  噪声方差估计  自校正Wiener状态估值器

英文关键词  multisensor information fusion  decoupled fusion  identification  noise variance estimation  self-tuning Wiener state estimator

中文摘要

      对含未知噪声方差阵的多传感器系统, 用现代时间序列分析方法, 基于滑动平均(MA)新息模型的在线辨识和求解相关函数矩阵方程组, 可得到估计噪声方差阵估值器, 进而在按分量标量加权线性最小方差最优信息融合准则下, 提出了自校正解耦信息融合Wiener状态估值器. 它的精度比每个局部自校正Wiener状态估值器精度高. 它实现了状态分量的解耦局部Wiener估值器和解耦融合Wiener估值器. 证明了它的收敛性, 即若MA新息模型参数估计是一致的, 则它将收敛于噪声统计已知时的最优解耦信息融合Wiener状态估值器, 因而它具有渐近最优性.一个带3传感器的目标跟踪系统的仿真例子说明了其有效性.

英文摘要

      For the multisensor systems with unknown noise variance matrices, using the modern time series analysis method, based on on-line identification of the moving average (MA) innovation model and the solution of the matrix equations for correlation function, the estimators of noise variance matrices can be obtained. Under the linear minimum variance optimal information fusion criterion weighted by scalars for components, a self-tuning decoupled information fusion Wiener state estimator is presented. Its accuracy is higher than each local self-tuning Wiener state estimator. It realizes the decoupled local Wiener estimators and decoupled fusion Wiener estimators for the state components. Its convergence is proved, i.e. if the parameter estimation of MA innovation model is consistent, then it will converge to the optimal decoupled information fusion Wiener state estimator with known noise variance matrices, so that it has asymptotic optimality. A simulation example for a target-tracking system with 3 sensors shows its effectiveness.