Optimal fusion filtering for systems with stochastic parametric uncertainties and packet dropouts

The distributed optimal fusion problem for the state estimation of multi-sensor discrete-time systems with stochastic parametric uncertainties and packet dropouts was studied.By introducing fictitious noises,the original system was transformed into an equivalent system without uncertain parameters.For each subsystem of the equivalent system with packet dropouts,the local filtering estimate and the local filtering error covariance were obtained by using the innovation analysis method.After the filtering error cross-covariance matrices between local estimates were obtained,the distributed optimal(i.e.,linear minimum variance) fusion filters were developed by the fusion rule weighted by matrices.The simulation example showed that the fusion filter was better than each local filter.