A modularized real-time indoor navigation algorithm on smartphones
Bibliographic record
Abstract
This paper outlines an indoor navigation algorithm that uses multiple kinds of sensors and technologies, such as 9-axis sensors (i.e., 3D gyros, accelerometers, and magnetometers), WiFi, and magnetic matching. The corresponding real-time software on smartphones includes modules such attitude determination and gyro bias estimation, pedestrian dead-reckoning (PDR), WiFi positioning, and magnetic matching. The heading from the attitude-determination module is fed into the PDR-based position-tracking module. Then, PDR is used for providing continuous position solutions and for the blunder detection of both WiFi fingerprinting and magnetic matching. Meanwhile, WiFi fingerprinting utilizes a point-by-point matching technology, while magnetic matching is based on profile-matching. Finally, WiFi and magnetic matching results are passed into the position-tracking module as updates. This algorithm was tested with two smartphones in two indoor environments. The results indicates the proposed navigation algorithm provided better navigation results than those of PDR, WiFi, or magnetic matching by itself, and better than the results of PDR/WiFi and PDR/magnetic matching (MM) in challenging indoor environment. The proposed using off-the-shelf sensors available in consumer portable devices and existing WiFi infrastructures, and have been realized on smartphones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".