A Hybrid WiFi/Magnetic Matching/PDR Approach for Indoor Navigation With Smartphone Sensors
Bibliographic record
Abstract
This paper presents a hybrid pedestrian navigation algorithm based on investigation of different combinations of pedestrian dead-reckoning (PDR), WiFi fingerprinting, and magnetic matching (MM). A multilevel quality-control mechanism is developed based on the interaction between different techniques. The algorithms were evaluated by walking in two indoor environments, with two smartphones, and under four motion conditions (i.e., handheld, at an ear, dangling with hand, and in a pants pocket). It was found that 2D accuracy of WiFi fingerprinting and MM is related with received signal strength and magnetic distribution, respectively. MM results had small errors on some occasions but suffered from significant mismatches. WiFi-aided MM provided better results than either WiFi or MM, but still had a risk of mismatching. Furthermore, integration of PDR, WiFi, and MM reduced dependency on both navigation environment and motion condition. The proposed algorithm provided more reliable solutions than both PDR/WiFi and PDR/MM, especially in areas with poor WiFi signal distribution or indistinctive magnetic features.
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 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 | 0.000 |
| 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 | 0.000 |
| 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".