<title>Geolocation of multiple emitters in the presence of clutter</title>
Bibliographic record
Abstract
In geolocating by time difference of arrival (TDOA), an array of sensors at known locations receive the signal from an emitter whose location is to be estimated. Signals received at two sensors are used to obtain the TDOA measurement. A number of algorithms are available to solve the set of nonlinear TDOA equations whose solution is the emitter location. An implicit assumption in these algorithms is that all the measurements obtained are from a single emitter. In practice, however, one has to deal with measurement origin uncertainty, which is a result of either multiple emitters being present in the region of interest, or clutter returns. In this paper, a method to determine the location of multiple emitters in a cluttered environment is presented. Several unmanned aerial vehicles (UAVs) are assumed as receivers of the electromagnetic emission from the emitter. Emissions received by different UAVs are used to obtain the TDOAs. Using a constrained optimization procedure, measurement-to-emitter associations are determined. Then, the resulting nonlinear equations are solved to find the emitter locations. An Interacting Multiple Model (IMM) estimator is used to track the located sources and to obtain their motion parameters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.034 |
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".