Every airliner, business jet, helicopter and many drones announce themselves on 1090 MHz. The hard part is not collecting those dots — it is answering the real questions about your patch of sky: what is that aircraft, is it behaving normally, and is anything pretending to be something it is not. Here is exactly how AeroScope does it.
After the fusion, scoring and anomaly stages run, the result lands on screen as a live instrument HUD over your position — a rotating radar dome, an animated altitude histogram, and a drag-to-explore situational globe with connection arcs to the contacts that matter. Rendered entirely client-side with three.js.
AeroScope is built around four questions an observer actually asks when something flies overhead. Everything in the platform maps back to one of them.
Pick any point on Earth — your home, an airport, a border. AeroScope pulls live traffic for that exact area and glides every aircraft smoothly across the map at its true ground speed.
OBSERVER-RELATIVERegistry lookups, type and operator, military hex ranges, emergency squawks and Remote-ID drone signatures turn an anonymous dot into an identified airframe with context.
ENRICHEDA DO-260B-style integrity check plus a Kalman innovation test and self-consistency residuals expose messages that contradict physics or their own integrity fields — the signature of spoofing.
INTEGRITYGeofences, emergency-squawk triggers and a transparent 0–100 attention score push the few aircraft that matter to the top — so you watch the sky, not a spreadsheet.
REAL-TIMEA lightweight position refresh keeps markers gliding every few seconds, while a heavier enrichment-and-detection pipeline runs on its own cadence — so deep analysis never freezes the map.
60+ crowdsourced ADS-B feeds plus a satellite-ADS-B merge, fetched per active map region.
→De-duplicate by ICAO 24-bit address; reconcile terrestrial and satellite views of the same airframe.
→Registry, Kalman tracks, DO-260B integrity, pattern-of-life and the 8-factor attention score.
→Six independent detectors vote; a flag fires only when at least two agree.
→WebSocket + REST to a client that dead-reckons each marker for continuous motion.
→The detection layer is deliberately built from established, auditable libraries — River, scikit-learn, pykalman, OpenAP, stumpy and PyOD — not an opaque neural network. Every flag traces back to the features that produced it. There is no PyTorch and no reinforcement learning in the deployed system; an earlier RL prototype was evaluated and retired in favour of this transparent stack.
Conventional flight trackers are built around the journey of a named flight. AeroScope inverts that: you fix a location, and the platform continuously evaluates everything within range of it. That single change is what makes spoofing detection, drone-candidate flagging, restricted-airspace proximity and pattern-of-life analysis meaningful — they are all relative to a place you care about.