PEDESTRIAN AND VEHICULAR NAVIGATION UNDER SIGNAL MASKING USING INTEGRATED HSGPS AND SELF CONTAINED SENSOR TECHNOLOGIES
Bibliographic record
Abstract
The performance enhancements achievable under signal masking such as urban canyons, indoor and under the forestry canopy with High Sensivity GPS (HSGPS) technology are first described using sample field results. HSGPS uses a longer integration time in order to utilize signals that are 25-30 dB weaker than the nominal line-of-sight GPS signals. A self-aided implementation of HSGPS that does not require external aiding from an existing network was tested. Depending on the type of signal masking, availability increases substantially. However, this occurs at the cost of increased susceptibility to interference which leads to very large measurement errors in some cases. Many tests obtained under a variety of conditions and summarized in the paper demontrate this clearly. In order to improve HSGPS overall performance (availability, accuracy, and reliability), integration with self-contained portable, preferably low cost sensors, is described. These sensors include miniature accelerometers, gyros, and six degrees of freedom IMUs. A performance analysis of vehicular and pedestrian applications of HSGPS conducted under a variety of conditions shows the advantages and limitations of self-contained sensor augmented HSGPS.
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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 | 0.001 |
| 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".