Ultra-wideband (UWB) precise location problem under signal interference based on Shark optimization algorithm
Bibliographic record
Abstract
In indoor positioning applications, UWB technology can achieve centimeter-level positioning accuracy, and has good resistance to multipath interference and weakness, as well as strong penetration. However, due to the complex and changeable indoor environment, UWB communication signals are easily blocked. Although UWB technology has penetration capability, it still produces errors. When there is strong interference, fluctuations of will occur, and indoor positioning cannot be basically completed, or even serious accidents will occur. Therefore, the problem of ultra-wideband (UWB) precise location under signal interference becomes an urgent problem to be solved. An algorithm is established to find the correct distance between the abnormal data processed by the minimum sum of absolute distance differences and normal data. Optimal target optimization A model based on shark optimization and UWB localization based on least square method are used to establish a comparison model, using shark optimization model can better calculate the exact location of Tag point.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".