MLA-MFL: A Smartphone Indoor Localization Method for Fusing Multisource Sensors Under Multiple Scene Conditions
Bibliographic record
Abstract
Scene-oriented multisource sensor fusion for smartphone pervasive indoor localization is the key to location-based services (LBS), which is of practical significance to addressing the limitations of indoor navigation satellite signals and facilitating accurate location services within the final 100 m. The rapid advancement of smartphone sensors and their performance provide a great opportunity for realizing smartphone indoor localization based on multisource sensors. However, the limited adaptability of current localization methods hinders their widespread applicability, necessitating the development of a smartphone-based indoor localization method tailored for complex indoor scenes. This article proposes a smartphone indoor localization method that integrates map location anchors (MLAs) with multisensor fusion location (MFL). The method identifies the sensor signal feature patterns of the smartphone’s built-in sensors in multiscenes and binds them to MLA to serve map matching. The MLA is also utilized to correct the cumulative error of pedestrian dead reckoning (PDR) to achieve indoor localization in multiple scenes by using fusion scheduling of different sensor modules. The experimental results show that the proposed method can achieve a localization accuracy of 1.01 m in multifloor scenes in collaboration with multisource sensor localization modules matched with MLA, with high robustness and usability. The code of this article is open source athttps://github.com/GHLJH/MLA-MFL.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".