Kalman filter based ranging and clock synchronization for ultra wide band sensor networks
Bibliographic record
Abstract
This Thesis presents the design, implementation, and validation of a Kalman filterbased range estimation technique to precisely calculate the inter-node ranges of Ultra Wide Band (UWB) modules. In addition to that the development and validation of an improved global clock synchronization framework is presented. Noise characteristics of relative time measurements of a stationary UWB anchor pair are first analyzed using an Allan deviation plot. To track the propagation of the imprecise clocks on low cost UWB transceiver platforms, Kalman filters are used in between every anchor pair. These filters track the variation of a remote anchor’s hardware clock relative to it’s own hardware clock, while estimating the time of flight between the anchor pair as a filter state. While adhering to a simple round robin transmission schedule, both inbound and outbound message timestamp data are used to update the filter. These measurements have made the time of flight observable in the chosen state space. A faster relative clock filter convergence has been achieved with the inclusion of the clock offset ratio as a measurement additional to the timestamps. Furthermore, a modified gradient clock synchronization algorithm is used to achieve global clock synchronization throughout the network. A correction term is used in the gradient clock synchronization algorithm to enforce the global clock rate to converge at the average of individual clock rates while achieving asymptotic stability in clock rate error state. Stability of the original and modified methods for time invariant hardware clocks are compared using eigenvalue tests. Experiments are conducted to evaluate synchronization and ranging accuracy of the proposed range estimation approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".