Fast RSSD multi-target localization in NLOS environments
Bibliographic record
Abstract
Signal strength–based localization is commonly employed in wireless sensor networks due to its low complexity and simplicity. However, in non-line-of-sight (NLOS) environments with unknown transmit power, effective and efficient multi-target localization is a challenging task. In this paper, a fast multi-target localization based on a neural network (FMLNN) is proposed. The received signal strength difference (RSSD) is employed and NLOS bias is considered. Determining the maximum likelihood (ML) estimator is a complex and highly non-convex problem, so it is solved indirectly using a neural network. First, prior data composed of known target information and RSSD values are used in offline training to learn the nonlinear relationship. Then, the locations of multiple targets are estimated online using the trained network. Results are presented which show the proposed method provides fast and efficient localization of multiple targets, and has greater robustness to NLOS bias than conventional state-of-the-art methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".