# Inter-Spacecraft Rapid Transfer Alignment Based on Attitude Plus Angular Rate Matching Using Q-Learning Kalman Filter

## TL;DR

This paper introduces a new method for aligning spacecraft using machine learning to improve accuracy and speed.

## Contribution

A novel Q-learning Kalman filter is proposed for rapid transfer alignment between spacecraft.

## Key findings

- The new attitude plus angular rate matching scheme improves transfer alignment performance.
- Q-learning Kalman filter outperforms standard and adaptive Kalman filters in simulations.
- A fifteen-dimensional model estimates attitude and calibration parameters simultaneously.

## Abstract

This study focuses on the transfer alignment issue between a master spacecraft and a slave spacecraft for the scenario in which the slave spacecraft is mounted on the master satellite before release and should be ready to depart and perform its space mission independently. The challenge of the transfer alignment is to estimate the attitude and calibration parameters of the gyroscope unit (GU) on the slave spacecraft based on the attitude determination system (ADS) of the master spacecraft. To improve the accuracy and rapidity of the transfer alignment, a novel attitude plus angular rate matching scheme is presented using fused sensor information on the master spacecraft. Accordingly, a fifteen-dimensional state-space model is derived to estimate the spacecraft attitude, the GU bias, scale factor error and misalignment simultaneously. A Q-learning Kalman filter (QKF) is designed to fine tune the process noise covariance matrix related to the calibration parameters, which benefits the state estimation performance. The simulation results show that the presented attitude plus angular rate matching scheme performs better than the traditional attitude matching scheme, and the QKF outperforms the standard Kalman filter (KF) and the adaptive Kalman filter (AKF).