Asymptotic Gradient Clock Synchronization in Wireless Sensor Networks for UWB Localization
Bibliographic record
Abstract
Time-of-flight-based localization requires high-accurate synchronization among the nodes in a network. Gradient clock synchronization (GCS) is a class of distributed algorithms capable of providing the demanded accuracy. Global clock rates defined by GCS algorithms are susceptible to drift relative to the individual hardware clock rates. This is due to the lack of a hard tie to a physical clock rate and is identified as the chaotic clock rate phenomenon. This scales range of measurements and can lead to stability issues in the network. This article presents a novel GCS algorithm for ultrawideband (UWB) ranging networks, addressing the chaotic global clock rate phenomenon. A correction term is introduced in the generic GCS algorithm so that the global clock rate is guaranteed to be converging into the average of individual clock rates. This also achieves asymptotic stability in the clock rate error state. The stability of the generic GCS and the proposed method for time-invariant hardware clock rates are compared using eigenvalue analysis in the clock error state space. A Kalman filter-based technique is used to precisely estimate the interanchor clock dynamics and ranges, which are then used in the asymptotic GCS (AGCS) update rule to calculate synchronization parameters. Simulations and experiments are conducted to evaluate the stability and synchronization accuracy of the proposed algorithm. The localization accuracy is evaluated for an indoor quadcopter localization task, which uses a range-assisted inertial navigation system (INS), resulting in rms position errors in the order of 0.2 m.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".