摘要
对于带未知噪声方差的多传感器系统,用相关方法给出了噪声方差的在线估值器,进而基于Riccati方程和按分量标量加权最优融合规则,提出了自校正分量解耦信息融合Kalman滤波器.用动态误差系统分析方法证明了自校正融合Kalman滤波器按实现收敛于最优融合Kalman滤波器,因而具有渐近最优性.一个3传感器跟踪系统的仿真例子说明了其有效性.
For the multisensor systems with unknown noise variances, an on-line noise variance estimator is presented by using the correlation method. Based on the Riccati equation and optimal fusion rule weighted by scalars for state components, a self-tuning component decoupled information fusion Kalman filter is presented. It is proved that the self-tuning fusion Kalman filter converges to the optimal fusion Kalman filter in a realization, so that it has the asymptotic optimality. A simulation example for a tracking system with 3-sensor shows its effectiveness.
出处
《控制与决策》
EI
CSCD
北大核心
2008年第2期195-199,203,共6页
Control and Decision
基金
国家自然科学基金项目(60374026)
