Asynchronous Acoustic Localization and Tracking for Mobile Targets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, acoustic-based indoor localization has attracted much attention due to its affordable infrastructure costs and high localization accuracy. However, previous work is infeasible in mobile target tracking for its long latency in obtaining sufficient beacon messages. In addition, the performance can further deteriorate due to device diversity, varying channel gains, and background noises. To this end, we propose an asynchronous acoustic localization and tracking system (AALTS), which utilizes distributed acoustic anchor nodes to locate passive off-the-shelf mobile devices. In AALTS, we propose an orthogonal chirp spread spectrum (OCSS) modulation technique, which doubles the data rate and thus mitigates the latency. We design a more robust method to capture acoustic signals which embody timestamps for localization, accounting for device diversity, varying channel gains, and the multipath effect. Finally, we incorporate an acoustic Doppler speed estimation module with a path-based particle filter framework to accurately track the moving targets. We have evaluated AALTS in an indoor testbed of size 8×12 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with commodity mobile phones and customized acoustic anchors. Our evaluation results demonstrate remarkable performance: AALTS achieves 90-percentile tracking errors of 0.49 m for mobile targets and a median of 0.12 m for stationary ones with only four anchor nodes.
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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 it