High-Accuracy Localization Platform Using Asynchronous Time Difference of Arrival Technology
Bibliographic record
Abstract
Despite extensive research efforts on ranging and localization modeling and simulation, research on practical implementations is limited. For the first time, a complete prototype based on asynchronous time difference of arrival (A-TDOA) technique is implemented in hardware. The A-TDOA technique requires neither clock synchronization between a target and anchor nodes nor wired infrastructure among anchor nodes, both of which are necessary for time of arrival and TDOA systems, respectively. All subsystems, including transmitter, receiver, antenna, and baseband processing unit, are developed from scratch and undergone significant updates for improved reliability. The implemented system has been extensively tested in an outdoor and indoor line of sight radio environments, and the accuracies obtained are 20.7 and 15.2 cm in 8 m × 8 m and 6 m × 6 m areas, respectively. In nonline of sight indoor environment, the achieved accuracy is 21.3 cm in 5 m × 5 m area. The comparison with the literature published to date proves the excellent quality of these results. To better understand the localization accuracy, the error sources due to thermal noise, hardware limitation, and radio propagation channel are identified and investigated. Mitigation methods are proposed to reduce errors. The implemented prototype supports many unique applications including cargo tracking, tourist guiding, emergency evacuation, and so on.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".