An Indoor Ultrasonic Positioning System Based on TOA for Internet of Things
Bibliographic record
Abstract
With the development of Internet of Things, the position information of indoor objects becomes more important for most application scenarios. This paper presents an ultrasonic indoor positioning system, which can achieve centimeter-level precise positioning of objects moving indoors. Transmitting nodes, receiving nodes, and display control terminal are needed to constitute the entire system. The system is based on long-baseline positioning technology that uses code division multiplexing access mechanism. There is no limit to the number of receiving nodes as the system works in the up-transmit-down-receive mode. Positioning of a receiving node is found based on ultrasonic Time of Arrival ranging technology. To accurately determine the positioning, there must be at least four or five transmitting nodes. The working radius will not be less than 5 meters when the height is larger than 3 meters. The system uses wideband pseudorandom noise signal called Gold sequences for multiuser identification and slant range measurement. The paper first gives a brief introduction of popular indoor ultrasonic positioning methods and then describes the theory of proposed algorithm and provides the simulation results. To examine the correctness of the approach and its practicality, the practical implementation and experimental results are provided also in the paper.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".