A Baseband MLE for Snapshot GNSS Receiver Using Super-Long-Coherent Correlation in a Fractional Fourier Domain
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Bibliographic record
Abstract
<h3>Abstract</h3> Low-cost global navigation satellite system and global positioning system(GPS) receivers require reliable baseband processing to guarantee accurate positioning. However, classic baseband performance is limited in challenging cases due to the characteristics of traditional loop filters. Accordingly, a snapshot baseband maximum likelihood estimator (MLE) using super-long coherent integration (S-LCI) in a fractional Fourier domain (FrFD) is proposed to upgrade the traditional frequency/phase/delay lock loop tracking algorithms. First, applying the S-LCI correlation in an FrFD increases the accuracy of a weak and dynamic signal estimation. Tolerance of the initial guess error in the snapshot baseband processing is then relaxed by the MLE. Finally, a gradient descent algorithm accelerates the convergence of signal estimation. Moreover, we derive the Cramer-Rao lower bound for the proposed MLE. Both numerical simulations and real-world experiments based on this GPS receiver prototype verify the effectiveness of its high-accuracy estimations of weak signals, strong tolerance for large initial guess errors, and prompt responses to converging.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it