Simultaneous Localization and Identification With Single-Source Resonant Beam
Bibliographic record
Abstract
Coupled with identification, 3D positioning can significantly enrich location-based services. Resonant beam (RB) is emerging as a promising solution to indoor positioning due to its self-aligning and energy-focused transmission. We propose a system for simultaneous localization and identification using RB as the individual medium. The base station (BS) employs a single-source RB, and each mobile target (MT) is equipped with a signal reflection module. For 3D localization, the BS estimates direction by analyzing RB’s spatial distribution and determines the distance from its frequency components. For identification, the passive MT captures the RB for power and reflects its identity (ID) to BS as spot flicker signals. To demonstrate the working principles, we have developed models for location estimation and ID recognition, as well as the power flow within the RB channel. In implementation, we incorporate a threshold regulation scheme for accurate image signal retrieval, along with an input power distribution model tailored for multi-access scenarios. Through simulating the entire process, we verify the system’s feasibility, including confirming the viability of ID recognition. We also evaluate localization performance, averaging ~ 1 cm at heights of 1.5 m ~ 2.5 m, showing promise for a wide range of potential applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".