Active Learning-Based Input Design for Angle-Only Initial Relative Orbit Determination

TL;DR

This paper introduces an active learning-based input design method to improve angle-only initial relative orbit determination, enabling more reliable autonomous rendezvous in space missions.

Contribution

It presents a hybrid estimation and control framework that enhances observability and initial state estimation for angle-only orbit determination, integrating active learning with EKF and MPC.

Findings

Outperforms baseline excitation strategies in simulations

Successfully resolves scale ambiguity in orbit estimation

Enables end-to-end autonomous rendezvous from initial estimation

Abstract

Accurate relative orbit determination is a significant challenge in modern space operations, particularly when relying only on angular measurements. The inherent observability limitations of this approach make initial state estimation difficult, directly impacting mission safety and performance. This work proposes a hybrid estimation and control strategy for autonomous rendezvous. An active learning (AL) based algorithm designs the initial input control sequence by maximizing the exploration of the output space, thereby enhancing the observability of the initial relative state for the angle-only initial relative orbit determination (IROD) problem. The IROD solution provides a batch estimate of the initial relative state and its analytical covariance, which quantifies the estimation quality and determines the transition point to recursive filtering. Once the uncertainty is sufficiently low, an Extended Kalman Filter (EKF) is initialized with the IROD solution and takes over for sequential estimation, providing state estimates to a Model Predictive Controller (MPC) to complete the rendezvous. The proposed framework is validated through numerical simulations, demonstrating its ability to reliably resolve the scale ambiguity, outperform baseline excitation strategies, and successfully execute an end-to-end rendezvous from initial estimation to final approach.