Reflection Map Construction: Enhancing and Speeding Up Indoor Localization
Bibliographic record
Abstract
This paper introduces an indoor localization method that utilizes fixed reflector objects within the environment, leveraging a base station (BS) or users equipped with Angle of Arrival (AoA) and Time of Arrival (ToA) measurement capabilities. The localization process consists of two phases. In the offline phase, using specific strategy effective reflector points within a specific region are identified. In the online phase, a maximization problem is solved to locate users based on BS measurements and information gathered during the offline phase. Through analysis and simulation results, we demonstrate that with the same number of training points, the performance of the proposed localization technique surpasses that of fingerprint-based techniques. Additionally, we show that localizing an unknown user does not require a large number of training points throughout the entire environment; it is sufficient to place these training points on the boundary of the environment. We introduce the reflectivity parameter ($n_{r}$), which quantifies the average number of first-order reflection paths from the transmitter to the receiver, and demonstrate its impact on localization accuracy. The log-scale accuracy ratio ($R_{a}$) is defined as the logarithmic function of the localization area divided by the localization ambiguity area, serving as an indicator of accuracy. We show that in scenarios where the Signal-to-Noise Ratio (SNR) approaches infinity, and without a line of sight (LoS) link,$R_{a}$is upper-bounded by$n_{r} \log _{2}\left ({{1 + \frac {\mathrm {Vol}({\mathcal {S}}_{A})}{\mathrm {Vol}({\mathcal {S}}_{\epsilon }({\mathcal {M}}_{s}))}}}\right)$, where$\mathrm {Vol}({\mathcal {S}}_{A})$and$\mathrm {Vol}({\mathcal {S}}_{\epsilon }({\mathcal {M}}_{s}))$represent the areas of the localization region and the area containing all reflector points with a probability of at least$1 - \epsilon $, respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".