Wavelet Analysis For Improving INS and INS/DGPS Navigation Accuracy
Bibliographic record
Abstract
The integration of the Global Positioning System (DGPS) with an Inertial Navigation System (INS) has been implemented for several years. In an integrated INS/DGPS system, the DGPS provides positions while the INS provides attitudes. In case of DGPS outages (signal blockages), the INS is used for positioning until the DGPS signals are available again. One of the major issues that limit the INS accuracy, as a stand-alone navigation system, is the level of sensor noise. The problem with inertial data is that the required signal is buried into a large window of high frequency noise. If such noise component could be removed, the overall inertial navigation accuracy is expected to improve considerably. The INS sensor outputs contain actual vehicle motion and sensor noise. Therefore, the resulting position errors are proportional to the existing sensor noise and vehicle vibrations. In this paper, wavelet techniques are applied for de-noising the inertial measurements to minimize the undesirable effects of sensor noise and other disturbances. To test the efficiency of inertial data de-noising, two road vehicle INS/DGPS data sets are utilized. Compared to the obtained position errors using the original inertial measurements, the results showed that the positioning performance using de-noised data improves by 34%–63%.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".