Multi-model and multi-sensor information fusion Kalman smoother
多模型多传感器信息融合Kalman平滑器

Based on the optimal information fusion algorithm weighted by scalars in the linear minimum variance sense,a distributed information fusion fixed-lag Kalman smoother weighted by scalars is given for discrete linear stochastic system with multiple model and multiple sensors.It only requires the computation of scalar weights,so that the calculated burden in the fusion center can be reduced.The information fusion steady-state smoother weighted by scalars is also given when all subsystems have steady-state filtering.It has a small calculation and is convenient to apply in real time.The computation formula for the smoothing error cross-covariance matrix is given between any two subsystems.A simulation example shows its effectiveness.