Joint Indoor Localization and Radio Map Construction with Limited\n Deployment Load
Bibliographic record
Abstract
One major bottleneck in the practical implementation of received signal\nstrength (RSS) based indoor localization systems is the extensive deployment\nefforts required to construct the radio maps through fingerprinting. In this\npaper, we aim to design an indoor localization scheme that can be directly\nemployed without building a full fingerprinted radio map of the indoor\nenvironment. By accumulating the information of localized RSSs, this scheme can\nalso simultaneously construct the radio map with limited calibration. To design\nthis scheme, we employ a source data set that possesses the same spatial\ncorrelation of the RSSs in the indoor environment under study. The knowledge of\nthis data set is then transferred to a limited number of calibration\nfingerprints and one or several RSS observations with unknown locations, in\norder to perform direct localization of these observations using manifold\nalignment. We test two different source data sets, namely a simulated radio\npropagation map and the environments plan coordinates. For moving users, we\nexploit the correlation of their observations to improve the localization\naccuracy. The online testing in two indoor environments shows that the plan\ncoordinates achieve better results than the simulated radio maps, and a\nnegligible degradation with 70-85% reduction in calibration load.\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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".