Novel velocity model to improve indoor localization using inertial\n navigation with sensors on a smartphone
Bibliographic record
Abstract
We present a generalized velocity model to improve localization when using an\nInertial Navigation System (INS). This algorithm was applied to correct the\nvelocity of a smart phone based indoor INS system to increase the accuracy by\ncounteracting the accumulation of large drift caused by sensor reading errors.\nWe investigated the accuracy of the algorithm with three different velocity\nmodels which were derived from the actual velocity measured at the hip of\nwalking person. Our results show that the proposed method with Gaussian\nvelocity model achieves competitive accuracy with a 50\\% less variance over\nStep and Heading approach proving the accuracy and robustness of proposed\nmethod. We also investigated the frequency of applying corrections and found\nthat a minimum of 5\\% corrections per step is sufficient for improved accuracy.\nThe proposed method is applicable in indoor localization and tracking\napplications based on smart phone where traditional approaches such as GNSS\nsuffers from many issues.\n
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".