An Unassisted Super-Resolution Satellite Navigation Receiver Using GPS L5 Signals
Bibliographic record
Abstract
Many existing works use external devices or information, e.g., real-world maps, reference receivers, dead reckoning (DR) sensors, and multiantennas, to compensate for signal dynamics and mitigate multipath interference in global navigation satellite system (GNSS) signal reception. This article proposes a standalone global positioning system L5 receiver using the fractional Fourier transform (FRFT) in an over-one-second superlong coherent integration (S-LCI) correlating process. Then, a code phase DR method is presented in the baseband processing by leveraging the super-resolution average Doppler rate and instantaneous Doppler frequency from the S-LCI-based FRFT to enhance the code discriminating process. A software-defined radio prototype of the unassisted super-resolution receiver (U-SRR) based on the S-LCI, FRFT, and code phase DR is developed where the pilot-channel L5 signal serves for positioning. The transmitting time splicing comes from the respective frame synchronization and code phase tracking in the data and pilot channels. Real-world experiments verify the proposed U-SRR in an extremely dense urban area. Compared to the legacy L1 coarse/acquisition (C/A) signals from the u-blox ZED-F9P receiver, the code-based positioning precision is increased by 86.8% with 100% measurement availability.
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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".