Two-step wireless positioning technique by exploitation of extended reference nodes
Bibliographic record
Abstract
Wireless positioning systems rely on a minimum number of transmitters with known locations as reference nodes to locate a target device. However, in harsh environments where the number of available reference nodes is inadequate, wireless positioning systems may suffer from significant performance degradation. In this paper, we propose a wireless positioning technique utilizing transmitting nodes from other co-existing networks to improve the locationing performance in terms of accuracy and coverage. The procedure of the proposed location technique can be divided into two steps. During the first step, locations of extended reference nodes from other co-existing networks - i.e. additional active transmitters external to the original positioning network - are collaboratively estimated by the original reference nodes. The positioning of the target node can then be improved by a combined use of the original and newly discovered extended reference nodes. Maximum likelihood (ML) principle is applied in both estimation steps. The position estimation performance is analyzed and compared with the Cramer-Rao Lower Bound (CRLB). Computer simulations show that the accuracy of the position estimation using this approach can be improved by the joint effort of the extended and original reference nodes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".