MULTILATERATION UNDER FLIP AMBIGUITY FOR UAV POSITIONING USING ULTRAWIDE-BAND
Bibliographic record
Abstract
Abstract. Ultrawide-band (UWB) ranging technology and multilateration techniques have recently been emerging solutions for positioning unmanned aerial vehicles (UAVs) in GNSS-denied environments. This solution offers cm-level ranging accuracy and considerable robustness to multipath receptions. UWB modules are commonly used in an anchor-based configuration; i.e., one UWB tag is mounted on the UAV, and several UWB anchors are installed on the ground. In real-world operational conditions, anchors can form a planar or a near-planar surface. This causes a geometric ambiguity, called flip ambiguity, in position estimation. Flip ambiguity can lead to considerable errors in the estimated position by multilateration. In this paper, we present a multilateration approach, which automatically resolves the flip ambiguity for UAV-positioning using UWB ranging. The proposed multilateration method first computes an algebraic solution through recursive least squares. If the initially estimated position is found to be flipped, then it is corrected by a symmetric reflection with respect to the anchor plane. Finally, the estimated position is refined by non-linear optimization. Extensive experiments in a real environment show that the proposed algorithm can effectively tackle the issue of flip ambiguity in multilateration, leading to a significant improvement in positioning accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".