Multi-sensor information fusion optimal Kalman filter for time-delay systems
多传感器时滞系统信息融合最优Kalman滤波器

Based on the optimal weighted fusion estimation algorithm with minimum variance,a non-augmentation dis- ributed weighted fusion optimal Kalman filter is given for discrete linear state time-delay stochastic systems with multiple sensors.The cross-covariance matrix of filtering errors between any two-sensor subsystems is derived for state time-delay systems.It has the same accuracy with weighted fusion filter with state augmentation.Compared with local filter based on each sensor,the distributed fusion filter has higher accuracy.Compared with the optimal filter with state and measurement augmentation,it has lower accuracy,but avoids the high-dimension computation and the large memory by augmentation, and has the reduced computational burden.A simulation example also shows its effectiveness.