AUGMENTED FAST ORTHOGONAL SEARCH/KALMAN FILTERING (FOS/KF) POSITIONING AND ORIENTATION SOLUTION USING MEMS-BASED INERTIAL NAVIGATION SYSTEM (INS) IN DRILLING APPLICATIONS
Bibliographic record
Abstract
Due to the advantages of small size and low cost, micro-electro-mechanical system (MEMS) inertial navigation systems (INS) show good prospects for use as a part of measurement-while-drilling (MWD) equipment to guarantee proper directional drilling procedure. Since current MEMS sensors have inaccurate measurements, an update aiding solution is developed using the Kalman filtering (KF) technique. However, because of the inherent poor behavior of MEMS sensors, KF technique with its linearized models has limited capability in providing accurate solution through the entire surveying process. In addition, certain realistic problems from the rugged environment would interrupt the updates in KF, without which the performance of the inertial system would deteriorate badly. This research proposes a fast orthogonal search (FOS)/KF solution where the FOS (a nonlinear modeling technique) method is proposed to augment KF. The experimental results illustrate that the FOS/KF solution outperforms the KF-only solution. Velocity and position performance are greatly enhanced during the interruptions of measurement updates.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".