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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".