HSGPS Signal Analysis and Performance Under Various Indoor Conditions
Bibliographic record
Abstract
High-Sensitivity GPS (HSGPS) receivers permit GPS signal measurements to be acquired and to track in certain indoor environments where previously not possible. However, position solution reliability and accuracy are significantly degraded due to the non-availability of some satellites, multipath errors, measurement noise associated with the low-power of the remaining signals, and echo-only and cross-correlation signal tracking. Provided that a GPS receiver has already acquired signals outdoor, additional information aiding is not a prerequisite for indoor tracking. The prime characteristics for indoor tracking are coherent signal integration and non-coherent signal accumulation over periods of up to several hundred milliseconds. The focus of this paper is to improve static mode HSGPS positioning performance in terms of accuracy and reliability and, thus, various techniques such as height fixing, simulation-based noise modeling, reliability and integrity testing, and batch processing are implemented in order to improve to positioning accuracy and reliability. A XTrac-LP(tm) (Extended Tracking - Low Power) HSGPS evaluation kit, developed by SiRF Technologies Inc., is used to investigate some indoor environments, namely a residential house and a concrete and steel commercial building, by analysing the fading and pseudorange error conditions. In addition, the measurements provided by the XTrac-LP(tm) receiver are used to test the performance of the positioning algorithms intended to improve the HSGPS positioning capability.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".