Direct estimation of time difference of arrival from compressive sensing measurements
Bibliographic record
Abstract
In many applications such as localization, there is a need to determine the unknown time shift D between a signal, and its time shifted version. Aligning one against the other until the two match will find D. When working with compressive sensing (CS) measurements, only linearly transformed samples of the signal and its time-shifted version are available. These CS samples conceal the explicit time shift relationship between the signals, and D can no longer be found by a simple alignment of the CS measurements. As a result, estimation of the time-difference-of-arrival (TDOA) from CS measurements requires the restoration of the original signals. The nonlinear restoration can be time consuming, and may introduce large errors when noise is present. This paper provides an alternate TDOA estimator that avoids restoration. The key is in making additional measurements to preserve the time shift relationship of the signals. This requires a slight modification of the random modulator pre-integrator, as described in the paper, which also includes a simulation example.
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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".