Indoor GNSS Signal Acquisition Performance using a Synthetic Antenna Array
Bibliographic record
Abstract
GNSS (Global Navigation Satellite System) signal reception in indoor environments is susceptible to spatial fading and signal attenuation. An antenna array utilizing spatial diversity can be implemented to improve detection performance which reduces the required fading margin. However for the typical handheld GNSS receiver, constrained to a single antenna, spatial processing gain is possible only if the antenna is physically translated as the signal is being captured by the receiver. This is equivalent to realizing a spatially distributed synthetic array (SA) antenna. An investigation of the indoor detection performance of a GNSS receiver based on SA processing with optimized combining algorithms is made and compared with the detection performance of the equivalent static antenna. The processing gain achievable through spatial combining of a synthetic antenna is considered from a general theoretical perspective and validated with an extensive set of experimental measurements satisfying statistical significance criteria. The performance of the proposed method is theoretically analyzed in terms of the probability of false alarm (PFA) and probability of detection (PD). It is shown that the significant processing gain resulting from randomly moving the antenna relative to a stationary position can be large, exceeding 10 dB in practically encountered usage cases for a GNSS handset.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".