3-D Indoor Positioning Based on Passive Radio Frequency Signal Strength Distribution
Bibliographic record
Abstract
In recent years, indoor positioning systems (IPSs) have received attention from many research fields, such as robotics, navigation, human–computer interaction, etc. However, IPS based on passive radio frequency (PRF) technology is still rare. This article proposes a 3-D IPS based on received signal strength (RSS) distribution and Gaussian process regression (GPR). Traditional RSS-based positioning systems have a transmitter with known frequencies, while in the proposed PRf signal of Opportunity—3D IPS (PRO-3DIPS), the system neither deploys new transmitters nor uses any a priori knowledge of transmitters. Furthermore, PRO-3DIPS integrates multiple Signal of Opportunity (SoOP) sources, shadowing, fading, and also captures scenario signatures. Data collection and analysis of PRF-based RSS distribution in 3-D space enables the capability of 3-D positioning. Three methods are applied and compared to find the frequency band most impacted by the scenario to achieve the best positioning performance as well as used in the estimation of RSS distribution. The RSS distribution is estimated by measuring the PRF spectrum on a fixed grid in the scenario. Using the RSS distribution, the GPR can accurately locate the receiver position. RSS at 90-gridded positions were collected in the experiment scenario, with one hundred samples at each position. The experimental result shows that a root-mean-square error (RMSE) of the proposed PRO-3DIPS is 0.292 m when the sampling distance is 1 m. The result demonstrates that the PRF spectrum is a new modality for the positioning task, which demonstrates better performance than most existing RF-based technologies.
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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 | 0.000 | 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".