Centimeter-Level Positioning Using an Efficient New Baseband Mixing and Despreading Method for Software GNSS Receivers
Bibliographic record
Abstract
This paper presents an efficient new method for performing the baseband mixing and despreading operations in a software-based GNSS receiver, and demonstrates that the method is capable of providing measurements for centimeter-level positioning accuracy. The method uses a single frequency carrier replica for the baseband mixing process, enabling all satellites to perform mixing simultaneously and yielding considerable computational savings. To compensate for signal-to-noise ratio (SNR) losses caused by using a single frequency carrier replica, the integration interval after despreading is divided into subintervals, and the output from each subinterval then compensated for the known frequency error. Using this approach, receiver processing times are shown to be reduced by approximately 21% relative to the next fastest method when tracking seven satellites. The paper shows the mathematical derivation of the new algorithm, discusses practical considerations, and demonstrates its performance using simulations and real data. Results show that the new method is able to generate pseudorange and carrier phase measurements with the same accuracy as traditional methods. Stand-alone positioning accuracy is at the meter level, while differential processing can produce fixed ambiguity carrier phase positions accurate to the centimeter level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".