Bibliographic record
Abstract
The Global Positioning System (GPS) is poised to play a critical role offering commercial opportunities in wireless communications as a result of the Federal Communications Commission's E-911 directive and the expansion of location-based mobile-commerce services (LBS). Successful E-911/LBS products and services will require solutions with features that can implement GPS in mobile phones with low cost, low power consumption, reasonable accuracy, high sensitivity and jamming immunity. Jamming immunity is a measure of the receiver's ability to provide GPS performance despite the presence of interfering signals anywhere else in the frequency spectrum. The ability of a GPS receiver to resist unwanted frequencies is a key measure of its performance. Applications involving cellular handsets provide a guaranteed source of potential jammers namely the cellular frequencies themselves. The aim of this paper is to analyze the effect of modulated signals such as amplitude modulated (AM) and frequency modulated (FM) signal sources on the GPS spectrum during the acquisition process. Interference signals cause distortion in the GPS signal resulting in an incorrect or no correlation peak during acquisition. A GPS simulator (GSS 6560) was used along with a signal generator (E 4431B) and an interference combiner (GSS 4766) to generate the interference signals. The signals were collected using a GPS hardware front end data logger (Signal Tap). A software GPS receiver was developed and used to analyze AM/FM interference effects. The adaptive predetection integration was used to reduce interference effects. Results show that adaptive predetection integration of up to 100 ms is sufficient to mitigate 20 dB relative AM interference power and 30 dB relative FM interference power.
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 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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.002 | 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".