An Autonomous Vision-Based Algorithm for Interplanetary Navigation
This addresses navigation challenges for autonomous deep-space missions, though it appears incremental as it builds on existing methods like extended Kalman filters with specific enhancements.
The paper tackles the problem of unsustainable radiometric tracking for deep-space probes by developing a vision-based navigation algorithm for autonomous interplanetary satellites, achieving applicability in a high-fidelity Earth-Mars transfer simulation.
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.