An Autonomous Vision-Based Algorithm for Interplanetary Navigation

TL;DR

This paper presents a fully autonomous vision-based navigation algorithm for interplanetary space probes, combining image processing, orbit determination, and an EKF to improve deep-space navigation accuracy and efficiency.

Contribution

It introduces a novel analytical measurement model and an optimal planet pair selection strategy, advancing autonomous navigation capabilities for deep-space missions.

Findings

Successful testing on Earth-Mars transfer simulation

Enhanced navigation accuracy with the new measurement model

Efficient computation using non-dimensional EKF

Abstract

The surge of deep-space probes makes it unsustainable to navigate them with standard radiometric tracking. Self-driving interplanetary satellites represent a solution to this problem. In this work, a full vision-based navigation algorithm is built by combining an orbit determination method with an image processing pipeline suitable for interplanetary transfers of autonomous platforms. To increase the computational efficiency of the algorithm, a non-dimensional extended Kalman filter is selected as state estimator, fed by the positions of the planets extracted from deep-space images. An enhancement of the estimation accuracy is performed by applying an optimal strategy to select the best pair of planets to track. Moreover, a novel analytical measurement model for deep-space navigation is developed providing a first-order approximation of the light-aberration and light-time effects. Algorithm performance is tested on a high-fidelity, Earth--Mars interplanetary transfer, showing the algorithm applicability for deep-space navigation.