THE UNIVERSITY OF CALGARY Selected GPS Receiver Enhancements for Weak Signal Acquisition and Tracking
Bibliographic record
Abstract
ii The sensitivity of a baseband signal processing unit, including both acquisition and tracking, is critical for a GPS receiver to function in perturbed signal enviroments such as indoors and under ionospheric scintillation.. To improve acquisition sensitivity, the dif-ferential combining approach, which allows a 2.5 dB improvement in processing loss as compared to noncoherent methods, is proposed herein. This results in a sensitivity im-provement ranging from 1.2 dB to 1.6 dB for a given probability of false alarm and mi-ssed detection. Based on an analysis of the Costas-family PLLs, carrier tracking is enhanced by optimiz-ing loop configurations. The decision-directed loop, exhibits a 2-dB sensitivity im-provement as compared to other types of PLLs. Bandwidth and root deviation caused by bilinear or boxcar transforms are solved by implementing a root-controled method. Using this design makes the bandwidth of the digital loop close to desired values, and thus im-proves carrier tracking by another 1 to 2 dB. A Kalman Filter adopts a soft-mode to deal with the bit sign uncertainty and adjusts the bandwidth to minimize the mean square car-rier tracking error. This leads to a 4 dB and 7 dB sensitivity improvement during weak and strong amplitude ionospheric scintillations, respectively. iii
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.013 |
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".