Decentralised moving-horizon state estimation for a class of networked spatial-navigation systems with random parametric uncertainties and communication link failures

This study is concerned with the navigational state estimation problem for a class of networked spatial-navigation systems subject to random parametric uncertainties (RPUs) and communication link failures (CLFs). The observations of the novel systems, comprising remote-access neighbours´ states and relative measures, are communicated through wireless packets, and thus are exposed to the impacts of RPUs and CLFs. In this study, a decentralised moving-horizon estimation approach is introduced to handle these ill-conditioned issues, and comprises two stages. First, a regional estimation is determined based on cooperative observations of spatial neighbours; and second, a collective estimation is derived through the fusion of regional estimation, local estimation and/or the cooperative estimations from spatial neighbours. Moreover, the resulting min-max problem is solved by a robust recursive scheme, which allows one to determine approximate estimates with a reduced computational condition. The convergence properties of the optimal estimator are also studied. The obtained stability condition implicitly establishes a relationship among the upper bound of estimation error, RPUs, and the CLF probability. Finally, an illustrative example of networked unmanned aerial vehicles (UAVs) is given to demonstrate the main features of the proposed estimator design approach.