A novel density-based geolocation algorithm for a noncooperative radio emitter using power difference of arrival
Bibliographic record
Abstract
This paper presents a novel density-based geolocation algorithm for locating a non-cooperative radio emitter using measurements of the power difference of arrival (PDOA), also known as received signal strength difference (RSSD). Consider a 2D space in a Cartesian coordinate system with N sensors and one stationary radio emitter and assume that the distance from a sensor to the radio emitter is the hypotenuse of a right triangle. For any combination of three sensors, there exists a system of three Pythagorean equations that can be transformed into a system of three circle equations whose centers and radii are related to the corresponding PDOA measurements. The intersections of the circles represent possible locations for the radio emitter. For N sensors, we can have a maximum of N(N-1) intersections of the circles. Dividing the 2D space into a grid, each grid cell contains a certain number of intersections. This method finds the grid cell with the highest intersection density and uses the center of this cell as the position fix estimate. MATLAB-based numerical simulations were used to evaluate the performance of this algorithm for various scenarios and 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".