Three dimensional compressed sensing for wireless networks‐based multiple node localization in multi‐floor buildings
Bibliographic record
Abstract
Abstract In wireless network‐based node localization, the received signals are hampered by complex phenomena, such as shadowing, noise, and multi‐path fading. In this work, the localization is stated as an ill‐posed problem that can be solved by compressed sensing (CS) technique. A three dimensional (3D)‐CS approach using the ratio of received signal strength (R2S2) and the time difference of arrival metrics was proposed to improve the localization accuracy of multiple target nodes in 3D wireless networks, and to reduce deployment complexity and processing time. Simulation and experimental tests were conducted in a large multi‐floors building using the strength of the received signals and the radio map of the localization area. The results indicated that the 3D‐CS approach is reliable for identifying the floor number and estimating the horizontal position. The localization precision is less affected by the propagation medium variation than the conventional 2D‐CS method. The localization mean error is lower when the number of access points increases, and the radio map spacing decreases. In addition, the accuracy of the 3D‐CS approach was assured as well as the building material characteristics, position of access points, and wireless‐terminal real transmission power are unknown. Copyright © 2015 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".