Threat & attention scoring
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.
An attention-ranker, demonstrated
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."
Why it's rule-based and explainable on purpose
Every point is traceable
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.
It ranks, it doesn't accuse
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.
Separate from detection
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).
How a score is actually assembled
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.
Why weights, and why these weights
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.
What the score is not
- Not a threat assessment. It ranks what deserves a look. It has no access to intent, tasking, flight plans or anything beyond public broadcasts.
- Not evidence. A high score is a prompt to read the contributing factors, nothing more.
- Not stable over time. It is recomputed every cycle, so an aircraft that scores highly while orbiting will fall back once it resumes transit.
- Not able to see silent aircraft. Anything not broadcasting is absent from the ranking entirely. See limitations.