Passive CubeSat Tracking:A Distributed Radiometric Approach to Tracking

Near-Earth Small Satellites

Benjamin KempkeUniversity of Michigan - MXL

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

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

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 doppler profile of each satellite, even after one month

• All three of these satellites were ejected at the same time

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

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

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

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

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

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

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

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

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

CONCLUSION