自校正信息融合Kalman预报器

Self-tuning Information Fusion Kalman Predictor

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.