Improving the Positioning Accuracy of DGPS/MEMS IMU Integrated Systems Utilizing Cascade De-noising Algorithm
Bibliographic record
Abstract
Global Positioning System (GPS) and Inertial Measurement Unit (IMU) augmented systems provide an enhanced navigation system that has superior performance in comparison with the stand-alone systems (i.e. GPS or Inertial Navigation System (INS)) as it overcome each of their limitations. Most GPS/IMU systems are integrated using the Kalman filter approach. In general, the quality of the final estimates of the state depends therefore on the quality of both the measurements being made and the models being used. Generally speaking, the long term errors of an INS can be reduced through the integration with GPS. On the contrary, the short term errors of an INS can be reduced by both the numerical integration process of the INS mechanization and pre-filtering the IMU raw data. Wavelet based denoising techniques have been applied to reduce the remaining short term errors. However, traditional wavelet denoising algorithm has certain limitations in removing undesired high frequency disturbance. Therefore, a novel denoising algorithm, cascade denoising algorithm, is presented in this article to overcome such limitations and improve the positioning accuracy during GPS outage. The proposed method has been tested using MEMS IMU data collected in land-vehicle.. The results demonstrated that the positioning accuracy during eight out of ten GPS outage periods was successfully improved from 5% to 20% using proposed cascade denoising technique.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".