ARTMS: Enabling Autonomous Distributed Angles-Only Orbit Estimation for Spacecraft Swarms

Abstract

This paper presents the design and expected performance of the Absolute and Relative Trajectory Measurement System (ARTMS), which is a software payload that provides autonomous, distributed navigation capability for spacecraft swarms in deep-space using low-cost optical sensors. This payload combines key innovations of recent works by the authors to overcome four major shortcomings of previous flight demonstrations of angles-only navigation: 1) lack of autonomy and reliance on accurate a-priori information from the ground, 2) inability to accommodate multiple observers and targets, 3) reliance on frequent translational maneuvers to improve observability, and 4) inability to estimate the absolute orbit of the observer satellite. The first innovation is a target detection and tracking algorithm that robustly identifies multiple targets across images from a single camera without requiring a-priori relative orbit knowledge. Second, a new batch orbit determination algorithm based on relative orbital elements provides estimates of the orbits of all spacecraft in the local swarm using a one-dimensional sampling scheme to accurately resolve the range to each target. Third, a sequential orbit determination algorithm based on the unscented Kalman filter continuously estimates the orbits and auxiliary parameters of the local swarm by seamlessly fusing measurements from multiple observers exchanged over an inter-satellite link. Functionality and performance of ARTMS are demonstrated through a sensor-in-the-loop simulation of fully autonomous angles-only orbit estimation using two observers in low earth orbit with no simplifying assumptions, which has never before been shown in literature. The results demonstrate that the ARTMS payload meets key needs of future deep space missions by providing autonomous, robust, and scalable absolute and relative navigation capabilities using low-cost hardware with minimal reliance on maneuvers and a-priori information.