Indoor GNSS Signal Acquisition Performance using a Synthetic Antenna Array
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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 ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FA</sub> ) and probability of detection ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</sub> ). 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.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it