Performance Bounds on Passive Indoor Positioning Using Visible Light
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, a novel method for passive indoor localization using LED luminaires is proposed where explicit user participation is not required. This approach measures changes in the impulse response between sources and receivers and estimates a location based on optical channel sounding data. An exponential integrating-sphere model is used to represent object impulse response (OIR) from each luminaire source-receiver pair, which is obtained by subtracting impulse response (IR) of the room background (i.e., without an object) from IR when the object is present inside the room. This model is represented as a function of 3D position of the object and depends on both source and receiver parameters and the physical geometry of the room. An analytical expression of the Cramér-Rao lower bound (CRLB) on the proposed passive indoor localization method is derived. The position is also estimated by using a maximum likelihood (ML) estimator which gives the position estimate by maximizing the log-likelihood function of the received noisy OIR waveforms. The results show that the signal-to-noise ratio (SNR) and number of source-receiver pairs used in the estimation, play a crucial role in performance. Typical localization root-mean squared error is less than 10 cm over a broad range of light intensities and object locations.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it