Synchronization of Weak Indoor GPS Signals with Doppler Using a Segmented Matched Filter and Accumulation
Bibliographic record
Abstract
This paper investigates GPS codephase acquisition with very low SNR signals found within large buildings. Conventional GPS receivers can acquire the GPS codephase relatively easily when in direct line of sight with the satellites. However, acquisition becomes difficult with highly attenuated signals found inside buildings. Recent government regulations aim to improve 911 service for wireless telephones by supplying dispatchers with caller location, however, this requires the development of new technologies. With extensive signal averaging, GPS signals can provide positioning within large buildings. We assume 5dB/floor of signal attenuation so the GPS signal would be -183 dBWor less at the bottom of a 5-story building. We assume 2 MHz system bandwidth and surrounding walls at room temperature of 290 K (17degC) so the Boltzmann noise is -141 dBW. More than 50 dB SNR improvement is provided by an accumulating segmented matched filter that is robust to polarity reversals caused by Doppler frequency offset and by GPS data transitions. Through simulation, performance comparisons are made to a conventional matched filter (CMF) operating without polarity reversals.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".