Integrated autonomous optical navigation using Q-Learning extended Kalman filter

Abstract

Purpose This paper aims to improve the performance of the autonomous optical navigation using relativistic perturbation of starlight, which is a promising technique for future space missions. Through measuring the change in inter-star angle due to the stellar aberration and the gravitational deflection of light with space-based optical instruments, the position and velocity vectors of the spacecraft can be estimated iteratively. Design/methodology/approach To enhance the navigation performance, an integrated optical navigation (ION) method based on the fusion of both the inter-star angle and the inter-satellite line-of-sight measurements is presented. A Q-learning extended Kalman filter (QLEKF) is designed to optimize the state estimate. Findings Simulations illustrate that the integrated optical navigation outperforms the existing method using only inter-star angle measurement. Moreover, the QLEKF is superior to the traditional extended Kalman filter in navigation accuracy. Originality/value A novel ION method is presented, and an effective QLEKF algorithm is designed for information fusion.

INS/CNS/DNS/XNAV deep integrated navigation in a highly dynamic environment

“…Dr Wang integrates the two methods and proposes a stellar refraction-aided SINS/CNS tightly integrated navigation method (Wang et al , 2020); its navigation accuracy greatly improves. Some scholars have also researched navigation filters (Qin et al , 2017; Hou et al , 2018; Xiong et al , 2022) to improve filter stability.…”