Research on the Improved Data Processing Method for Foot-Mounted Inertial Pedestrian Positioning System
Bibliographic record
Abstract
Foot-mounted Inertial Pedestrian Position System (FIPPS) plays an important role for indoor position application. It can be used in the environment without GNSS such as firefighting and military. However, the accumulating position error produced by the MIMU measurement noise, makes the system for long time position impossible. Zero Velocity Update (ZUPT) is a proposed algorithm to reduce position error for FIPPS. For ZUPT, Kalman filter is used to estimate and compensate the position error. However, because the heading misalignment angle cannot be observed by ZUPT, part of the position error caused by heading misalignment angle cannot be compensated. According to the problem above, this paper shows the improving FIPPS position algorithms we proposed in recent years, including: a) Adaptive Gradient Descent Fast-Initial Alignment Algorithm for solving the problem of the inaccuracy initial attitude resulting in the position error; b) the FR-data algorithm for solving the stable problem when the pedestrian walking fast; c) Adaptive Inertial/Magnetometer Positioning Algorithm and Improved Attitude Algorithm for improving the heading misalignment angle observability; d) Dual-foot positioning algorithm based on Adaptive Inequality Constraints Kalman filter for correcting the position error. Finally, performance of the FIPPS improve algorithm is tested using MTi-G710 MIMU.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".