Comparison of Three Filters Space Objects Parameters Estimation via Astrometric and Photometric Data

To ensure safety for in-orbital satellites, it is important to identify space objects. Therefore, the parameters estimation problem of space objects has attracted more and more research interests in aerospace engineering. The unscented Kalman filter (UKF) and the particle filter (PF) are two common estimation methods applied to solve this problem through nonlinear filtering techniques. However, UKF has the disadvantage of low estimation accuracy and divergence. The PF has a high computational cost when applied to estimate parameters of space objects. To solve those two drawbacks, the square root cubature Kalman filter (SRCKF) is applied, which can guarantee high estimation accuracy and robustness while still reducing the computational cost. Simulation results are presented to compare those three filters' parameter estimation performance of space objects. It is seen that the SRCKF achieves better estimation accuracy than the UKF. The SRCKF and the UKF have higher computational efficiency than the PF.