Hierarchical memory fusion algorithm for multi-sensor hybrid multiple model estimation
Qiao Xiang-dong · Systems engineering and electronics · 2011
First,two approximate methods for calculating overall priori information of local nodes with interacting multiple model filter are developed.Secondly,to obtain global priori information of fusion center,global posteriori model probabilities are got according to the evidence combination rule of Dempster-Shaffer evidence theory,and based on this,the concept of the global equivalent target model is proposed.Based on above results,it is possible to apply a hierarchical memory fusion algorithm to make fusion of multiple interacting multiple model estimations.To improve estimation performance of local nodes by using the fusion results,a feedback mechanism that transports global posteriori model probability back to local nodes is put forward.Simulation results demonstrate that hierarchical memory fusion algorithm of interacting multiple model estimation is valid,and the developed feedback mechanism can actually improve the estimation performance of local nodes.