Factor Graph Method for Target State Estimation in Bearing-only Sensor Network

In the process of target tracking and localization in bearing-only sensor network, it is an essential and significant challenge to solve the problem of plug-and-play expansion while enhancing the accuracy of state estimation and stability of the system. This paper proposes the distributed cubature information filtering method based on the two-layer factor graph. Firstly, the distributed measurement model of the bearing-only sensor network and the motion model of the maneuvering target are constructed, and by investigating the observability and the Cramer-Rao lower bound of the system model, the preconditions are analyzed. Subsequently, the localization factor graph and cubature information filtering algorithm of sensor node pairs are proposed for localized estimation. Building upon this foundation, the mechanism for propagating confidence messages within the fusion factor graph is designed, and is extended to the entire sensor network to achieve global state estimation. Finally, some groups of simulation experiments are conducted to compare and analyze the results, which verifies the rationality, effectiveness and superiority of the proposed distributed cubature information filtering method based on factor graph.