Strong tracking central difference Kalman filter for CubeSat attitude estimation with status mutation

Abstract

This paper addressed a strongly nonlinear problem caused by status mutation in CubeSat attitude estimation system. The multiple fading factors are employed to make different state channels have separate adjustment ability, which enhances the tracking performance for status mutation. The second-order difference transformation is adopted to improve the approximation accuracy of state posterior mean and covariance. Therefore, a multiple fading second-order central difference Kalman filter (MFSCDKF) is formed for CubeSat attitude estimation system in the presence of status mutation. Compared with the single fading factor first-order central difference Kalman filter, the proposed one can improve tracking performance and estimation accuracy simultaneously. Simulation results based on real telemetry data from the on-orbit CubeSat NJUST-1 verify the effectiveness of the proposed MFSCDKF.

Federated strong tracking filtering for nonlinear systems with multiple sensors

“…Especially for the third aspect, when a state mutation occurs after the system reaches a stable state, the gain matrix will be increased along with the generated residual. Up to now, for the state estimation problem of nonlinear systems, the published literature on the STF or its improved algorithms has been widely applied in various areas, such as unmanned technologies (Ping et al, 2020), satellites (Ma et al, 2021), and robots (Dai et al, 2019).…”