Year: 2021
Type: conference-paper
Abstract: The proliferation of deep-space probes is posing new challenges in navigating them with ground-in-the-loop methods.Ground tracking stations will reach saturation owing to the escalation of small satellites in deep-space.Thus, autonomous guidance, navigation, and control methods that are independent from ground communications are necessary for future deep-space satellites.For deep-space applications, planets are used as beacons to determine the observer state in deep-space using line-of-sight navigation and celestial triangulation.This paper introduces a planets line-of-sight extraction algorithm for deep-space autonomous navigation.In particular, the methodology focuses on image generation, image processing, and line-of-sight extraction.The probe attitude is estimated from generated images and, if a planet is detected, its line-of-sight is extracted to enable the autonomous satellite position estimation.Numerical simualtions show that a 3σ accuracy of 20 arcseconds for the planet line-of-sight can be reached, yielding to a 3σ accuracy of 1000 km for autonomous position estimation during deep-space application.
Authors: Sandra Bella, Eleonora Andreis, Vittorio Franzese, Paolo Panicucci, Francesco Topputo
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
Awards: G434337789, G7319883546
OpenAlex ID: W3207030500