Optimal Signal Sampling Configuration for MEMS INS/GPS Navigation
Bibliographic record
Abstract
For vehicle navigation, Global Positioning System (GPS) provides long term accurate measurements, but only when a direct line of sight to four or more satellites exists. Inertial navigation systems (INS), on the other hand, are self contained sensors that can provide short term measurements. The integration of the two systems can effectively provide continuous navigation data even during GPS signal outages. Traditional INSs are bulky and expensive, and therefore, can not be used for daily civilian applications. With the evolution of MEMS technology, MEMS-based INS sensors are evolving into more accurate, compact and inexpensive units. Hence, there is a growing interest in exploring the capabilities of these sensors in the field of vehicle navigation. Most of the research is targeted towards finding the best error models and integration techniques that can reduce the high drift and errors associated with these sensors. One of the important aspects of this integration is the optimal configuration for sampling frequency, number of bits and time delay during recording of the various sensor outputs. The very low cost of the MEMS sensors makes the cost of the signal sampling, i.e. analog to digital conversion (ADC), an issue. These parameters will reduce the on-board memory requirement, speed up the computation and hence, significantly reduce the final cost to the consumers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".