Hardware-in-the-loop Simulation of GNSS Signal Tracking in Highly Elliptical Orbits Using the GSNRx™ Software Receiver
Bibliographic record
Abstract
The University of Calgary PLAN group’s GSNRxTM software receiver has been updated for use in orbital simulations. The updates included redesigned algorithms for satellite visibility calculations, the inclusion of a Kepler orbit model, an updated channel allocation strategy, a navigation solution reset when an insufficient number of GPS satellites are being tracked, and disabling the tropospheric corrections in the navigation solution. The redesigned GSNRxTM has been used for hardware in the loop simulations of tracking and navigation in highly elliptical orbit (HEO). The HEO scenario has been set up based on the orbital parameters of the European Space Agency’s planned Proba-3 mission. A realistic link budget has been assumed, and error sources such as ionospheric delay and GPS orbital errors have been simulated. \n \nUnder the HEO simulation conditions, the receiver reliably acquires signals at 37 dB-Hz and stronger, and is able to maintain lock under HEO orbital dynamics. Loss of lock typically occurs between 28 and 30 dB-Hz for fading signals, with some signals tracked to 26 dB-Hz. The measurement noise is highly correlated with the highly variable HEO signal strength and is slightly higher than the theoretical thermal noise tracking jitter at the same carrier to noise density ratios. The receiver’s single point navigation solution agrees well with the simulated trajectory where ionospheric errors are small, but is highly sensitive to the ionospheric range delay on setting GPS satellites as positioning geometry weakens at high altitude. An initial filtered navigation solution was calculated using an extended Kalman filter to combine the GPS measurements with an orbital model. The filtered results were accurate to the meter level during the two perigee passages when position fixes were possible, and diverged to a few hundred meters during the 18 hour arc between fixes. Future work will include research into multi-constellation tracking, and relative positioning for formation flying satellites in HEO.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".