A neural network model of optical gyros drift errors with application to vehicular navigation
Bibliographic record
Abstract
Inertial navigation systems (INS) incorporating three mutually orthogonal accelerometers and three mutually orthogonal gyroscopes are integrated with global positioning systems (GPS) to provide reliable and accurate positioning information for vehicular navigation. Because of their high reliability and accuracy, ring laser gyroscopes (RLG) and fiber optic gyroscopes (FOG) are usually utilized inside most of the present INS. However, bias drift at the output of these optical gyroscopes may deteriorate the performance of the overall INS/GPS navigation system. This paper introduces a method to enhance the performance of optical gyros in two phases. The first phase utilizes wavelet multi-resolution analysis to band limit the gyro measurement and improves its signal-to-noise ratio. The second phase employs radial-basis function (RBF) neural networks to predict drift errors. The drift model provided by the RBF network is established using the gyro raw measurement and time as inputs and provides the drift error at its output. The RBF neural networks are utilized in this study since they generally have simpler architecture and faster training procedure than other neural network types. The proposed method is applied to E-core 2000 FOG (KVH Industries Inc., Rhode Island, USA).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".