UWB Positioning Using Six-port Technology and a Learning Machine
Bibliographic record
Abstract
This paper presents a short-range positioning system based on six-port technology and the corresponding signal processing algorithms. Accurate positioning with high resolution of detected targets is achieved by utilizing both the impulse signal and the wideband phase discrimination characteristic of a six-port circuit operating in ultra-wideband (UWB) mode. This system has several advantages such as object penetration, multipath immunity and low probability of interception. Signal processing includes range estimation and position location algorithms. The range is estimated by analyzing the output signal pattern with support vector machines (SVM). Based on the estimated ranges between a target node and access points, the position of the target is determined using hyperbolic position location method. Ranging and position simulation results of the considered system are given for an indoor multipath channel. Accurate positioning has been achieved with an average root mean square error of 0.68 cm. The simulation results also show the robustness of the proposed system in a multipath channel
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".