Decimeter Level Cooperative Direct Localization With Ising Model Approach
Bibliographic record
Abstract
With the ubiquitous availability of WiFi signals, WiFi-based localization methods have gained a lot of attention, especially in indoor environments. Generally, accurate indoor localization is challenging due to the multipath effect. Numerous methods have been proposed to increase the accuracy of localization for multipath environments. One of the recent methods is direct localization. This method includes a two-dimensional search in a planar geometry to directly localize the source without estimating any intermediate variable such as angle-of-arrival or time-of-flight. In this paper, we use a compressed sensing framework in the direct localization technique to estimate the location of a user in an indoor multipath environment. We form a penalized$\ell _{0}$-norm structure for this problem and then convert this structure to an Ising energy problem to take advantage of the efficient existing binary programming solvers. In this paper, the Ising energy problem is solved using Markov Chain Monte Carlo (MCMC). The evaluations show that our approach significantly improves the localization accuracy compared to other approaches in the literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".