Path loss exponent estimation and RSS localization using the linearizing variable constraint
Bibliographic record
Abstract
Received Signal Strength (RSS) localization has the attraction of requiring only simple receiver hardware. The basic idea is to find the position estimate that is most consistent with the received signal power measurements observed at multiple known locations in the area of interest. A fundamental assumption is that the signal power measurement is proportional to the product of the reference emitter power, n0, and a distance dependent term of the form d-α̅, where d is the emitter to receiver separation and α̅ is the path loss exponent. In practice, α̅ depends on the environment and can vary over α̅ large range. There is often very limited a priori knowledge of α̅ anḋ0available and the use of inaccurate values in the localization equations can adversely affect the accuracy of position estimates. This paper develops a new method, based on a linearizing variable constraint, for the estimation of α̅. The estimation of the emitter position and oḟ0then follows directly. This estimator yields a performance similar to a two-stage weighted least squares scheme, but requires much less computation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".