On distributed fusion estimation with stochastic scheduling over sensor networks

The paper deals with the distributed fusion estimation for linear time-varying systems over sensor networks, in which stochastic sensor scheduling and unknown exogenous inputs are taken into account. In the stochastic sensor scheduling, expensive and cheap channels are used to respectively transmit the high-precision data and the low-precision quantized data. Based on the stochastic scheduling scheme, a recursive minimum mean square error (MMSE) estimator is proposed against the unknown inputs. Then, a distributed fusion estimator is presented by combining local estimates and covariances from all sensors, relying on the covariance intersection (CI) fusion rule. Sufficient conditions are established to ensure that the proposed fusion estimator is stable with the stochastically ultimately bounded estimation error. Finally, a target tracking example is given to show the effectiveness of the proposed method.