Accurate Distance Estimation for RSS Localization With Statistical Path Loss Exponent Model
Bibliographic record
Abstract
We study the ratio-of-distance estimation from the difference of received signal strength (DRSS) when the source transmission power is unknown. Due to fluctuations in the wireless medium, the path loss exponent is also considered unknown and is modelled statistically as a uniformly distributed random variable. When maximum-likelihood estimation is employed, we are required to solve a highly non-linear function which contains a summation of exponential and Q-function terms in the log-ratio-of-distances. In the state-of-the-art [8], the Q-function terms are ignored in order to derive an approximate but erroneous solution in closed-form. In this work, we firstly expose the adverse impact of ignoring the Q-function terms on the estimation performance. Next, we propose a uniform search root finder (USRF) method which solves the original non-linear function without ignoring any of the underlying terms. Numerical studies reveal that the proposed method not only provides much more accurate ratio-of-distance estimates than the state-of-the-art, but also provides significantly better localization accuracy.
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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 | 0.004 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".