Low-Computation GNSS Signal Acquisition Method Based on a Complex Signal Phase in the Presence of Sign Transitions
Bibliographic record
Abstract
During signal acquisition in a global navigation satellite system acquisition stage, a method of signal parameters estimation with low computational complexity is needed. However, due to the influence of sign transitions, the correct peak corresponding to the signal parameters is difficult to detect. For the estimation of the code phase of the received signal in the presence of sign transitions with low computational complexity, an acquisition method based on a complex signal phase (AMCSP) is proposed. The problem of estimating the sign transition position and the code phase is transformed into a problem of solving for a complex signal phase. Special block matrixes are constructed to obtain the complex signal phase, and integration processing is utilized to improve the detection probability performance. Based on an analysis of undesirable cases, a final code phase estimation process is proposed. Furthermore, expressions for the detection performance and computational complexity of AMCSP are derived. Simulation results demonstrate that the computational cost of AMCSP is much lower than that of a fast Fourier transform-based method.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".