On distributed Kalman filter based state estimation algorithm over a bearings-only sensor network

This paper studies the distributed state estimation problem for a class of discrete-time linear time-varying systems over a bearings-only sensor network. A novel fusion estimation algorithm of the distance between the target and each sensor is constructed with the mean square error matrix of corresponding estimation being timely provided. Then, the refined estimation of distance is presented by minimizing the mean square error matrix. Furthermore, the distributed Kalman filter based state estimation algorithm is proposed based on the refined distance estimation. It is rigorously proven that the proposed method has the consistency and stability. Finally, numerical simulation results show the effectiveness of our methods.

Automated summary

This paper investigates the distributed state estimation problem for a class of discrete-time linear time-varying systems over a bearings-only sensor network. The novel fusion estimation algorithm is proposed to estimate the distance between the target and each sensor, with the mean square error matrix being taken into consideration. The refined distance estimation is then obtained by minimizing the mean square error matrix. Finally, the distributed Kalman filter based state estimation algorithm is proposed and its consistency and stability are rigorously proven. The numerical simulation results demonstrate the effectiveness of the proposed methods.