UNIVERSITY OF CALGARY Study of Interference Effects on GPS Signal Acquisition
Bibliographic record
Abstract
Interference and jamming is one of the major concerns in using the Global Positioning System (GPS) for critical applications. The GPS system has advantages over the narrow-band navigation systems since GPS signals are spread-spectrum signals and receiver design techniques can eliminate most of the interference signals. Any signal or its harmonics near the GPS L1 and L2 frequencies are a potential source of interference. The interference signals outside GPS frequency band can be filtered out either by a GPS antenna or a receiver front-end. Interference signals within the GPS frequency bandwidth are difficult to isolate using the filters. These signals need to be mitigated either by the acquisition process or the tracking process. This thesis investigates possible interference mitigation by the acquisition process. Acquisition methods were implemented as a part of the correlator in a software receiver and used for analysis. Interference resulting from sampling in the receiver front-end and cross-correlation between the GPS Gold codes were studied. Aliasing effect introduces a loss of 2-3 dB in the acquisition gain and causes false locks for smaller sampling frequencies at a wider precorrelation bandwidth. The cross-correlation between the GPS Gold codes causes problems for the signal acquisition below-135 dBm. Different radio frequency (RF) interference signals were studied to analyze their effect on the acquisition process. Adaptive predetection integration (up to 100 ms) was performed to determine the possible tolerance to the RF interference signals. A continuous wave (CW) interference hinders the acquisition more compared to any other RF signals such as swept CW, amplitude modulated (AM), frequency modulated (FM) or broadband noise. An RF signal level of 15-25 dB above the GPS signal level was found sufficient to jam the acquisition process. ii
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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".