PEDESTRIAN AND VEHICULAR NAVIGATION UNDER SIGNAL MASKING USING INTEGRATED HSGPS AND SELF CONTAINED SENSOR TECHNOLOGIES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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