快速信息融合Ka lman 滤波器

应用现代时间序列分析方法,在标量加权线性最小方差融合准则下,提出一种多传感器快速信息融合稳态Kalman滤波器.基于ARMA新息模型计算稳态Kalman滤波器增益,提出了计算传感器之间的滤波误差方差阵和协方差阵的Lyapunov方程,它可用迭代法求解,并证明了迭代解的指数收敛性.与基于Riccati方程按矩阵加权的信息融合Kalman滤波器相比,可明显减小计算负担,便于实时应用,可用于设计含未知噪声统计系统的信息融合自校正Kalman滤波器.最后以目标跟踪系统的一个仿真例子说明了其有效性.

Fast information fusion Kalman filter

By the modern time series analysis method, under the linear minimum variance fusion criterion weighted by scalars, a multisensor fast information fusion steady-state Kalman filter is presented, where the gain is computed via the autoregressive moving average(ARMA) innovation model. And Lyapunov equations are presented for computing the filtering error variance and covariance matrices among sensors, which can be solved by iteration. The exponential convergence of the iterative solution is proved. Compared with the Riccati equation-based information fusion Kalman filter weighted by matrices, it can obviously reduce the computational burden, and is suitable for real time applications, It can be applied to design information fusion self-tuning Kalman filter for systems with unknown noise statistics. A simulation example for a target tracking system shows its effectiveness.