Tightly Coupled UWB-INS Positioning With Passive Synchronization Using Continuous Clock Phase Tracking
Bibliographic record
Abstract
This work evaluates the effectiveness of a novel one-way time of flight-based passive ultrawideband (UWB) inertial navigation solution for indoor positioning of a mobile platform and formulates a design criterion for choosing between passive systems through noise analysis. The proposed tightly coupled time of arrival (TC-TOA) approach achieves passive synchronization by keeping track of the local clock and its derivatives as states. Using these states and position estimate, the reception timestamps of the network messages are predicted. The errors in these predicted timestamps are then used to update both clock states and the position states through accurate modeling of coupled interactions. The design results in a tightly coupled estimator formulation achieved using an error state Kalman filter with right quaternion error parameterization. The proposed method is evaluated using a MATLAB simulation environment and on a dataset acquired by flying a quadcopter in an indoor environment, with ground truth obtained from a motion capture system. Decawave DWM 1000-DEV hardware with custom firmware was used in the measurement acquisition process. Simulation results demonstrate that the proposed tightly coupled system outperforms time difference of arrival (TDOA)-based methods, especially when the network transmission gap becomes large or in the presence of communication interruptions, which can happen in large-scale networks. Experimental validation resulted in RMS-position errors around 25 cm, with increasing differences between the TC-TOA versus TDOA methods as the number of anchors drops. Furthermore, the proposed method can accommodate measurement updates even when the connection with the network is interrupted down to one anchor.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".