When dozens of aircraft are overhead, the question is not "where are they" but "which one deserves my attention first." AeroScope answers it with a transparent 0–100 score built from eight weighted, rule-based factors, and it always shows its working. It is a ranker, not a verdict.
The needle below cycles through example aircraft. In the live app it reflects real broadcasts, recomputed every cycle, with the contributing factors always displayed beside it.
A high score means "look here first," not "this aircraft is hostile."
The score is a weighted sum of named factors, so each aircraft's number decomposes back into exactly which signals fired and by how much. No black box.
The output sorts a busy list so an operator looks at the right aircraft first. A high score is a prompt for human judgement, never an automated allegation.
The attention score is distinct from the consensus anomaly detectors. Scoring decides order; detection decides whether a specific anomaly is real (≥2 detectors must agree).
Scores are additive and every contribution is displayed next to the aircraft, so the arithmetic can always be checked. Consider a real pattern: an aircraft with an ICAO address inside a known military block, flying a racetrack orbit at 22,000 ft roughly 15 nautical miles from your position, with no filed callsign.
| Observation | Contribution |
|---|---|
| ICAO hex sits in an allocated military block | Large. This is the single heaviest factor. |
| No callsign or registration resolves | Moderate. Common for state aircraft, so it corroborates rather than proves. |
| Racetrack orbit sustained over time | Moderate. Distinguishes loitering from transit. |
| Within tens of nautical miles of the observer | Small and distance-scaled. Proximity raises relevance, not menace. |
| Integrity fields consistent, altitude and speed plausible | Nothing added. The broadcast is internally coherent. |
The result lands in the elevated band, and the honest interpretation is "a state aircraft is loitering nearby". That is very often an air-refuelling track, a training area or a survey task. The score has done its job by moving one aircraft to the top of a list of two hundred. It has not made a judgement about intent, and it should not be read as one.
Three design choices matter more than the specific numbers.
Rules, not a learned model. A trained classifier could likely rank aircraft slightly better on a benchmark. It could not tell you why it ranked one first, and it would drift silently as traffic patterns changed. Every factor here maps to a stated rule you can disagree with. That trade, accepting a little accuracy for full auditability, is deliberate and is the same reasoning behind the torch-free anomaly stack.
Weights reflect discriminating power, not danger. A military hex allocation scores highly because it is rare and unambiguous, not because military aircraft are dangerous. Emergency squawks score highly because they are unambiguous declarations by the crew. Proximity scores low because almost everything is near someone.
Corroboration beats any single signal. The scale is built so that no lone factor reaches the elevated band by itself. Several independent indicators have to agree, which is the same consensus principle used in spoofing detection.