自校正信息融合Kalman预报器  

Self-tuning Information Fusion Kalman Predictor

作　　者：李春波[1] 邓自立[1] 

机构地区：[1]黑龙江大学自动化系,哈尔滨150080

出　　处：《科学技术与工程》2006年第5期513-518,共6页Science Technology and Engineering

基　　金：国家自然科学基金(60374026);黑龙江大学自动控制重点实验室基金资助

摘　　要：对含未知噪声统计的多传感器系统,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,可在线估计噪声统计,进而在按矩阵加权线性最小方差最优信息融合准则下,提出了自校正信息融合Kalman预报器。证明了它的收敛性,即它具有渐近最优性,且自校正融合Kal-man预报器比每个局部自校正Kalman预报器精度高。一个目标跟踪系统的仿真例子说明了其有效性。For the muhisensor systems with unknown noise statistics, using the modem time series analysis method, based on on-line identification of the moving average （MA） innovation models, and based on the solution of the matrix equations for correlation function, the noise statistics can on-line be estimated, and further under the linear minimum variance optimal information fusion criterion weighted by matrices, a self-tuning information fusion Kalman predictor is presented Its convergence is proved, it has asymptotic optimality, and its accuracy is higher than each local self-tuning Kalman filter. A simulation example for a target tracking system shows its effectiveness.

关 键 词：多传感器信息融合 矩阵加权融合 MA新息模型 系统辨识 噪声方差估计 自校正Kalman预报器 

分 类 号：O211.64[理学—概率论与数理统计]