Line-of-Sight Deep-Space Autonomous Navigation

TL;DR

This paper presents an extended Kalman filter-based autonomous navigation method for deep-space CubeSats using line-of-sight observations of Solar System objects, achieving 3-sigma accuracy of 1000km in position and 2 m/s in velocity.

Contribution

It introduces a novel EKF formulation leveraging line-of-sight data for deep-space navigation, accounting for various uncertainties and delays.

Findings

Feasibility demonstrated with 3-sigma position accuracy of 1000 km.

Velocity estimation accuracy of approximately 2 m/s.

Method accounts for sensor, attitude, and light-time delay uncertainties.

Abstract

Autonomous navigation is one of the main enabling technologies for future space missions. While conventional spacecraft are navigated through ground stations, their employment for deep-space CubeSats yields costs comparable to those of the platform. This paper introduces an extended Kalman filter formulation for spacecraft navigation exploiting the line-of-sight observations of visible Solar System objects to infer the spacecraft state. The line-of-sight error budget builds upon typical performances of deep-space CubeSats and includes uncertainties deriving from the platform attitude, the image processing, and the performances of the sensors. The errors due to the low-thrust propagation and light-time delays to the navigation beacons are also taken into account. Preliminary results show the feasibility of the deep-space autonomous navigation exploiting the line-of-sight directions to visible beacons with a 3-sigma accuracy of 1000km for the position components and 2 m/s for the velocity components.