MODELING AND NUMERICAL SIMULATION OF AN ALGORITHM FOR THE INERTIAL SENSORS ERRORS REDUCTION AND FOR THE INCREASE OF THE STRAP-DOWN NAVIGATOR REDUNDANCY DEGREE IN A LOW COST ARCHITECTURE
Bibliographic record
Abstract
In this paper, an algorithm for the inertial sensors errors reduction in a strap-down inertial navigation system, using several miniaturized inertial sensors for each axis of the vehicle frame, is conceived. The algorithm is based on the idea of the maximum ratio-combined telecommunications method. We consider that it would be much more advantageous to set a high number of miniaturized sensors on each input axis of the strap-down inertial system instead of a single one, more accurate but expensive and with larger dimensions. Moreover, a redundant system, which would isolate any of the sensors in case of its malfunctioning, is obtained. In order to test the algorithm, Simulink code is used for algorithm and for the acceleration inertial sensors modeling. The Simulink resulted sensors models include their real errors, based on the data sheets parameters, and were conceived based on the IEEE analytical standardized accelerometers model. An integration algorithm is obtained, in which the signal noise power delivered to the navigation processor, is reduced, proportionally with the number of the integrated sensors. At the same time, the bias of the resulted signal is reduced, and provides a high redundancy degree for the strap-down inertial navigation system at a lower cost than at the cost of more accurate and expensive sensors.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".