Melrose Brown, UNSW Canberra Space; Russell Boyce, UNSW Canberra Space; Andrew Lambert, UNSW Canberra Space; Edwin G. W. Peters, UNSW Canberra Space; Steve Gehly, UNSW Canberra; Samuel Boland, UNSW Canberra Space; Ryan Jefferson, UNSW Canberra Space; Anthony Kremor, UNSW Canberra Space; Timothy Bateman, UNSW Canberra Space; Chris Capon, UNSW Canberra Space; Brenton Smith, UNSW Canberra Space; George Bowden, UNSW Canberra Space; Lauren Glina, UNSW Canberra Space; Lily Qiao, UNSW Canberra Space; Sudantha Balage, UNSW Canberra; Komal Gupta, UNSW Canberra Space; Rizka Purwanto, UNSW Canberra; Travis Bessell, Clearbox Systems; Tom Reddell, LeoLabs; James Bennett, EOS Space Systems; Michael Lachut, EOS Space Systems; Tim McLaughlin, Pine Park Engineering Corp
Keywords: formation flying, characterisation, optical, passive RF, orbit determination
Abstract:
The University of New South Wales (UNSW) embarked on an ambitious CubeSatellite research, development, and education program in 2018 through funding provided by the Royal Australian Air Force (RAAF). M1 (Mission 1), M2 Pathfinder, and M2 are the satellite missions included in the project. M2 is the series’ final mission, and it contains two 6U CubeSatellites flying in formation using differential aerodynamic drag control. The M2 satellites were launched in a conjoined 12U form factor on RocketLab’s ‘They Go Up So Fast’ launch in March 2021. On 9 September 2021, the spacecraft divided into two 6U CubeSats (M2-A and M2-B) under the action of a small spring force in their near-circular 550km, 45-degree inclination orbit. The formation is controlled by varying the spacecrafts’ attitude, which creates a substantial variation in the aerodynamic drag force due to the change in cross-sectional area due to the large, double-deployable, solar arrays located on the nominal zenith face of the spacecraft.
This paper presents the outcomes of the Formation Flying and Change Detection primary mission objectives for the mission. The results are generated by collecting and analysing optical and RF (Radio Frequency) space domain awareness sensor data from the ground and validating them against GPS (Global Positioning System) and attitude data downlinked from the spacecraft. The outcomes of the broader mission objectives, which include increasing the Technology Readiness Level for a suite of intelligent on-board optical and RF sensor technologies, will be presented in subsequent publications.
The results presented here comprise two major campaigns: 1.) The spacecraft separation campaign when the original 12U form factor deployed following launch split in half to form the M2-A and M2-B satellites, and 2) the demonstration of active formation control of the spacecraft via differential aerodynamic drag.
M2-A and M2-B underwent several significant configuration changes during the spacecraft separation campaign. The results from ground-based sensors detecting the 12U spacecraft separating into two distinct (6U) objects are presented. The effect of the double-deployable solar arrays deployment on the relative orbital motion of the M2-A and M2-B spacecraft is illustrated and compared to data from optical and RF ground-based measurements taken during this window.
The formation control campaign involved actively controlling the spacecraft via differential aerodynamic drag in order to significantly alter the separation distance. The mission demonstrated the capability to switch the leading spacecraft’s position between M2-A and M2-B and to actively control separation distance ranging from 130km down to 2km. Formation control is achieved via open-loop, pre-scheduled, commands issued from the UNSW Canberra Space ground station. A two-stage modelling and simulation process is used to derive the scheduled attitude states. Firstly, a batch least squares orbit determination algorithm is applied to GPS data from a steady-state differential drag actuation period (where one spacecraft is in maximum drag and the other in its minimum drag attitude configuration). The batch least squares orbit determination is conducted out using the NASA General Mission Analysis Tool (GMAT), resulting in precise state estimates for each spacecraft and drag coefficient estimates for both the maximum and minimum drag configurations. Predictions of trajectory for various attitude profiles can be produced by tailoring the spacecraft’s drag coefficients between the maximum and minimum values generated by the batch least squares state estimation process.
Ground-based optical and RF space domain awareness (SDA) sensor measurements collected during the manoeuvre campaign are compared to the spacecraft’s GPS and attitude telemetry data. The SDA sensors are actively seeking to detect changes in the separation distance between the spacecraft. Initial results from an investigation into whether changes observed in photometric light curve signatures can signal the commencement of a differential drag manoeuvre are presented.
Date of Conference: September 27-20, 2022
Track: Space-Based Assets