Optimum reference node deployment for TOA-based localization
Bibliographic record
Abstract
While achieving localization using wireless networks, the positioning accuracy of a target device is highly sensitive to the placement of reference nodes. As a result, an in-depth analysis on the optimal placement of reference nodes is extremely useful in order to improve deployment outcome. In this paper, we propose an optimum reference node deployment scheme for Time of Arrival (TOA)-based localization by minimizing the Cramer-Rao Bound (CRB) of localization error. In order to find the global minima of the CRB which is highly nonlinear, a novel method is developed to solve the corresponding optimization problem. The essence of our method is to express the CRB in complex coordinates, and then to minimize the CRB with respect to the angles of reference nodes. The mathematical solution provides an interesting result indicating that the highest localization accuracy is achieved when the reference nodes have uniform angular distribution around the service area where the target is expected. We compare several different reference node deployment schemes through simulations, and the results show our derived optimum deployment provides the best performance.
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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".