Passive CubeSat Tracking: A Distributed Radiometric Approach to Tracking Near-Earth Small Satellites

1. Passive CubeSat Tracking:A Distributed Radiometric Approach to Tracking Near-Earth Small Satellites Benjamin Kempke University of Michigan - MXL

2. The Problem – I Can’t Find My Satellite! • CubeSats are usually flown as secondary payloads • Multiple CubeSats in the same launch dropped off in nearly identical orbits • The CubeSats, along with other debris from the rocket are initially given anonymous classifications from NORAD • Object A, B, C, etc. • It is up to the satellite operators to distinguish which object is which • LEOTrack aims to help out with these issues, along with providing a mechanism for generic day-to-day satellite tracking

3. An Example: Recent Launch of M-Cubed, RAX-2, and E1P • Six unidentified objects to start • Two quickly classified with help from high-gain dish • RAX-2 took about a week to get a firm determination • Remaining three objects took approximately one month to classify • One issue was that one object was likely just debris • M-Cubed and E1P stuck together

4. Current Heuristic Classification Methods • Differing orbits will result in different profiles of the observable Doppler shift during a pass of the satellite over a ground station • This spectrum, derived from tracking AubieSat-1, shows deviations in the Doppler profiles of nearby E1P and M-Cubed • Still fairly hard to make out the exact dopplerprofile of each satellite, even after one month • All three of these satellites were ejected at the same time

5. Current Heuristic Classification Methods Aren’t Good Enough • No systematic way of distinguishing the satellites • Current approaches rely on ‘eye-balling’ it • Require at least 5-10km in spacecraft separation to produce a distinguishable difference in Doppler estimates • It can take weeks for the spacecraft to separate this far, wasting valuable time for spacecraft checkout and operations • It would be great to add GPS or use current deep-space tracking techniques, but these significantly increase system cost and complexity

6. How Did We Fix It? • Accurate tracking using Doppler-based methods requires extremely accurate (<1Hz) measurement • Even the spacecraft transmissions do not maintain this level of stability over time • Need a way to cancel out these effects • LEOTrack is set up to estimate pairwise difference-based Doppler measurements between ground stations • Accurately determining the time-of-arrival of a signal is also valuable, but these estimates are inherently noisy • LEOTrack also estimates these difference-based time-of-arrival (ToA) measurements

7. Moving Complexity to the Ground • Still need a stable clock and time reference at each station, but now this burden has been shifted away from spacecraft designers • Multiple ground stations are synchronized together using GPS • Software-defined radio (SDR) architecture is required in order to record the necessary raw baseband measurements • Each time a transmission is heard at a listening ground station, the raw baseband data along with any pertinent timing information is forwarded to a central server for pairwise analysis

8. LEOTrack Algorithm Basics • LEOTrack constructs pairwise diff-Doppler and diff-ToA estimates for each transmission received at listening ground stations • LEOTrack works in tandem with an orbit determination (OD) filter to iteratively improve an estimate of the satellite’s orbit • An Extended Kalman Filter (EKF) in Monte (JPL-propriety software suite) processes the noisy measurements and provides updated estimates of the satellite’s orbit

9. First Step: ‘Coarse Acquisition’ • First step is to clean up the data received from independent ground stations • Retimes and resamples all baseband data so that respective samples from each ground station line up in time • All non-transmission data is also discarded • The data is also transformed to remove any expected time- and Doppler-shift from each station’s viewpoint using the current estimate of the satellite’s orbit • Assuming the orbital model is perfect, the transformed baseband data from each station should be identical • Any remaining errors will be determined in the ‘Fine Acquisition’ step

10. Second Step: ‘Fine Acquisition’ • Here, the diff-Doppler and diff-ToA estimates are refined for each pair of ground stations • Exhaustive searching of the entire {diff-Doppler, diff-ToA} subspace is time consuming • diff-Doppler is searched first, then diff-ToA • Search space is defined by the uncertainty imposed by the current orbit model estimate • Resulting point of max correlation corresponds to the final estimate

11. Last Step: Uncertainty Estimation & OD Filter Update • OD Filter requires variance estimates for all measurement inputs in order to know how much to ‘trust’ the measurement • diff-Doppler uncertainty is simple as it depends only on the observed signal and noise power of the transmission • diff-ToA uncertainty is more complex and dependent upon the actual message being transmitted • Can say for certain that if the message is decoded, it is less than the bitrate of the message

12. Contributions This Summer • Created baseband data simulation tools • Developed and evaluated LEOTrack in a variety of operational scenarios • Created the necessary interfacing between LEOTrack and Monte • Result: It works! • Graphs to the right show pre-fit and post-fit absolute position error • Position error has been reduced from a maximum of ~3km to ~0.42km • Faster anonymous object classification • Better accuracy than TLEs

13. Next Steps • Supporting ground station hardware development – realizing the system • Collect and process ‘real’ data • Further development and evaluation of LEOTrack for different application domains • Higher orbits and/or deep-space trajectories

14. Conclusion