Probabilistic Source Localization Based on Time-of-Arrival Measurements
Bibliographic record
Abstract
This article was motivated by the need to localize a sensor (signal source) in an Internet-of-Things (IoT) network to an area with a predetermined probability (credibility). The source is assumed to transmit a short time duration (burst) signal in a homogeneous line-of-sight environment. The source's signal is known to an array of spatially separated receivers, which can measure the times of arrival of the source's signal subject to Gaussian measurement errors. The time that the signal leaves the source (i.e., the transmit time) is, however, unknown. The authors develop a method for computing the exact a posteriori probability density functions (pdfs) of the coordinates of the source's in both 2-D and 3-D spaces. The obtained a posteriori pdfs incorporate arbitrary a priori densities, which makes them very useful in many practical scenarios. Unlike existing point-estimate methods, the probabilistic method does not simultaneously solve a set of equations so there is neither a lower nor upper limit on the number of receivers. Various examples are provided to demonstrate the superiority and usefulness of the proposed method. In particular, it is shown that the joint a posteriori pdf of the target's location is not always approximately jointly Gaussian, especially when the target is in close proximity to one of the gateways, or when the gateways are located in close proximity to each other.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".