Cooperative localization of mobile nodes in NLOS
Bibliographic record
Abstract
In this paper, cooperative localization of mobile nodes in non-line of sight (NLOS) situation is considered using a constrained square root unscented Kalman filter (CSRUKF). The NLOS measurements are used as quadratic constraints, which form a convex feasible region inside which the positions of the mobile nodes are supposed to be. The CSRUKF consists of two main stages: square root unscented Kalman filter (SRUKF) and sigma point projection. In the former, a conventional SRUKF is used to estimate the state vector and the Cholesky factor of the error covariance matrix. In the latter, a new set of sigma points are generated, and the ones violating the constraints are projected onto the feasible region by solving a set of convex quadratically constrained quadratic programs (QCQP). Each QCQP can be solved independently and in parallel for each sigma point violating the constraint, thus the algorithm is suitable for distributed processing. The simulation results show that our algorithm can perform well in different NLOS scenarios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".