Sensor localization in NLOS environments with anchor uncertainty and unknown clock parameters
Bibliographic record
Abstract
In this paper, joint sensor localization and synchronization in non-cooperative wireless sensor networks (WSNs) using time-of-arrival (TOA) measurements is studied. In addition to zero-mean errors in TOA measurements we consider other sources of error such as non-line-of-sight (NLOS) propagation and anchor uncertainty to make our technique more useful in practice, where the presence of these errors is inevitable. The proposed technique is based on semi-definite programming (SDP) relaxation which can be solved in polynomial time and guarantees convergence to the global minimum. It is shown that the optimal accuracy is obtained by discarding the NLOS measurements and applying the maximum likelihood (ML) technique to jointly estimate the positions of anchor nodes, and the position and clock parameters of the sensor node. The results show that the proposed SDP technique, which does not require prior identification of NLOS links, is robust against NLOS errors and its performance is close to that of optimal accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".