A Navigation Accuracy Evaluation Method for Multi-path Platform Inertial Navigation System
Bibliographic record
Abstract
The purpose of this study was to investigate estimation of the navigation accuracy of the highprecision platform inertial navigation system (PINS) rapidly. However, the prediction accuracy often plunges deeply when the model is trained by numerous flight paths. The PP-LSSVM approach was adopted to solve the problem with sparse solutions. The highdimensional input data were dimensionally reduced by the principal component analysis (PCA); The sparsity of the model was improved by the pruning algorithm, aiming to reduce the computing load and prediction time. Thus, the proposed model is denoted as the PP-LSSVM. The results obtained in this study include the PP-LSSVM outperformed the LSSVM in prediction time by an order of magnitude, while satisfying the accuracy requirement. The results indicated that the research provides a suitable evaluation model for navigation accuracy of multi-path PINS.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".