Modified Iterated Square-root Cubature Kalman Filter for Non-cooperative Space Target Tracking

Chaochen Wang, Panlong Wu, Yuhao Deng

Passive tracking techniques for non-cooperative space target have great significance in space surveillance systems. In this paper, we proposed a new filtering algorithm for passive tracking problem called iterated square-root cubature Kalman filter (ISCKF). By introducing a Newton-Gauss iterative method into the square-root cubature Kalman filter (SCKF), the proposed filtering algorithm has a better filtering performance in accuracy and stability. The simulation results demonstrate that the ISCKF outperforms the conventional filters when using bearings-only measurements.