Decentralized State Estimation Algorithm of Centralized Equivalent Precision for Formation Flying Spacecrafts Based on Junction Tree

Abstract

As centralized state estimation algorithms for formation flying spacecraft would suffer from high computational burdens when the scale of the formation increases, it is necessary to develop decentralized algorithms. To the state of the art, most decentralized algorithms for formation flying are derived from centralized EKF by simplification and decoupling, rendering suboptimal estimations. In this paper, typical decentralized state estimation algorithms are reviewed, and a new scheme for decentralized algorithms is proposed. In the new solution, the system is modeled as a dynamic Bayesian network (DBN). A probabilistic graphical method named junction tree (JT) is used to analyze the hidden distributed structure of the DBNs. Inference on JT is a decentralized form of centralized Bayesian estimation (BE), which is a modularized three-step procedure of receiving messages, collecting evidences, and generating messages. As KF is a special case of BE, the new solution based on JT is equivalent in precision to centralized KF in theory. A cooperative navigation example of a three-satellite formation is used to test the decentralized algorithms. Simulation results indicate that JT has the best precision among all current decentralized algorithms.

Robust decentralised state estimation for formation flying spacecraft

“…We compare the (decentralised EKF (DEKF) same as the original reduce‐order EKF referenced in [9]) the proposed method DHCF with iteration, DHCF without iteration and the decentralised cubature Kalman filter (DCKF) in which the measurement update of each deputy relies on original cubature Kalman filtering. In addition, the result of DCKF with Gaussian measurement noise is also given and named as CKF‐Gauss in the figures.…”

“…Unfortunately, as the number of the spacecraft information becomes larger, the computation burden of the chief becomes unbearable due to its requirement of large state covariance and measurement matrix calculation [6]. To overcome this problem, the decentralised solutions are designed [7–9]. Compared to the centralised solutions, the decentralised solution allocates the computational load among all the spacecrafts in the formation by gathering the measurements and estimating the state in a decentralised way (i.e.…”

“…In most studies of spacecraft formation relative navigation, proprioceptive sensors (e.g. global positioning system GPS) are used to determine the absolute/relative state vectors [9–11] due to their advantages in providing absolute measurements. However, GPS receiver is not suitable for high earth orbit (HEO) and deep‐space formation flying because the GPS signal is not available in these cases [8, 12].…”

An Improved Unscented Kalman Filter Algorithm for Radar Azimuth Mutation

“…UKF uses UT transform to process the nonlinear transmission of mean and covariance, so its accuracy is higher than EKF [26]. Furthermore, UKF is widely used in target tracking, vehicle driving state estimation, navigation systems, and aircraft attitude estimation [27,28]. Last but not the least, UKF overcomes the shortcomings that EKF is easy to diverge and has been fully applied in engineering practice.…”