Novel Iterative Approach for Time-of-Arrivals-Based Localization With Maximum Likelihood Estimation
Bibliographic record
Abstract
In this letter, the problem of localizing in a wireless environment with non-line-of-sight (NLoS) propagation without strict time synchronization is being considered. This problem utilizes maximum likelihood (ML) estimation based on time-of-arrival (TOA) measurements. However, the original form of this problem is not convex and cannot be directly solved using standard convex optimization techniques. To overcome this issue, we decompose the original non-convex localization problem into two convex sub-problems: quadratic programming (QP) and linear regression (LR). The QP sub-problem is formulated by converting the numerator of ML expression into the quadratic form and regarding the nonlinear denominator as a weight. In contrast, the LR sub-problem is built using the nonlinear relation among the defined variables. Through a dynamic adjustment of the objective function in the QP sub-problem, we develop an iterative algorithm to solve the original localization problem. The simulation results demonstrate that the proposed algorithm achieves the same localization accuracy with reduced computational complexity compared to the ML estimation-based localization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".