Lower bounds on mobile terminal localisation in an urban area
Bibliographic record
Abstract
Lower bounds on localisation errors serve as a performance indicator of how close a localisation system is to providing optimal performance. In this study, localisation of mobile terminals for urban areas is performed using received signal strength (RSS)-based techniques with maximum likelihood (ML) and linear kernel (LK) estimators. Simulations are performed with and without buildings in an urban area cell to illustrate the effect of discontinuities in the RSS profiles, on radio location accuracy. Results show that a localisation error is higher when buildings are absent as compared to the scenario when buildings are present. Buildings add extra features to the RSS measurement space which, if known to the localisation system, improve radio location accuracy. A comparison is made between the root mean-square error of the ML and LK estimators with the Cramer–Rao bound (CRB), the Bayesian Cramer–Rao bound (BCRB) and the Weiss–Weinstein bound (WWB). These comparisons show that the previously used CRB and BCRB do not provide realistic lowers bounds in the presence of buildings. In such cases bounds, such as WWB, which are capable of handling RSS discontinuities provide more realistic lower bounds on the accuracy of radio location.
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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 | 0.010 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".