Visible Light Passive Indoor Tracking Using Background Subtraction
Bibliographic record
Abstract
Most of the currently used active visible light positioning approaches require the involvement of the target itself in the positioning and are very susceptible to changes in the background. This paper proposes a passive visible light indoor tracking approach that assumes no direct interaction between the target and the luminous elements, and is shown to be robust against background changes by introducing the concept of background subtraction (BS) to fix the issues of background changes. Using background subtraction, the background is dynamically updated without any prior information about such changes. The position estimate is determined by estimating the object impulse response (OIR) which is the reflections from the target alone after having subtracted the background from the measured impulse responses (IRs) at every time scan. Positioning is accomplished by selecting the position with the maximum likelihood (ML) between the difference signal and the OIR. A converted measurement Kalman filter (CMKF) is used for tracking the target moving under a nearly constant velocity model. The simulation results ensure accurate localization and tracking based on the proposed approach with positioning and speed RMSEs of 3 cm and 1.5 cm/s, respectively, under severe background changes.
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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 | 0.001 | 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".