Decentralized Moving Horizon Estimation for Networked Navigation System with packet dropouts
Shuai Liu, Guorong Zhao, Yunfeng He, Chao Gao · 2020
This paper is concerned with state estimation for networked navigation system with packet dropouts. The packet dropout phenomenon is modeled by a stochastic variable satisfying the Bernoulli binary distribution, the predictor of the lost measurement is used as a compensator when packets dropout occur. At each local estimator, moving horizon strategy is applied to update the state, in which only cooperative packets and relative measurements over the most recent time horizon are used. Then the fusion estimator is obtained by applying the covariance intersection criterion. Finally, an illustrative example is given to demonstrate the efficiency of the proposed method.