自校正信息融合Kalman平滑器

Self-tuning information fusion Kalman smoother

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,得到了噪声统计的在线估值器,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平滑器,提出了一种按实现收敛性新概念,证明了自校正Kalman融合器按实现收敛于最优Kalman融合器,因而它具有渐近最优性．同单传感器自校正Kalman平滑器相比,它可提高平滑精度,一个目标跟踪系统的仿真例子说明了其有效性．

For the multisensor systems with unknown noise statistics, using the modem time series analysis method, based on the on-line identification of the moving average （MA） innovation models, and based on the solution of the matrix equations for correlation function, the on-line estimators of noise statistics are obtained. Furthermore, under the linear minimum variance optimal information fusion criterion weighted by matrices, a self-tuning information fusion Kalman smoother is presented. A new concept of the convergence in a realization is presented, and it is proved that the self-tuning Kalman fuser converges to the optimal Kalman fuser in a realization, so that it has the asymptotic optimality. Compared with the single-sensor self-tuning Kalman smoother, its accuracy is improved. A simulation example for a target tracking system shows its effectiveness.