Near-Ground Airspace Intelligence Network

Data status: Illustrative

Close to the ground, line of sight breaks all the time. Terrain, buildings and clutter cut every view into pieces, and a single node either drops the track when it disappears or invents one to keep the picture whole. Neither is honest, and the second looks better than the first.

• Complementary viewpointsObservations of the same space from more than one position, so that what one cannot see another may. No site or layout is described here.
• Radio-frequency and optical tracesIndependent traces of low-altitude activity that do not depend on the same line of sight.
• Environmental contextConditions that change what is visible close to the ground, such as haze and clutter.

1. Combines complementary viewpoints with optical and radio-frequency traces.
2. Maintains continuity through an occlusion as an explicit inferred hypothesis, drawn dotted and labelled as inferred.
3. Lets environmental context adjust confidence, and widens the band while a track is inferred.

• Inferred and observed are styled differently: dotted against solid, with a text label as well.
• A denser evidence problem, handled by a synthesis of sensing, awareness and context.
• A second viewpoint restores an observed track, and the page says when that has happened.
• No track is invented to keep a picture tidy.

Near-ground airspace intelligence is the synthesis of the portfolio. It draws on distributed passive viewpoints, optical and radio-frequency traces, fusion and atmospheric context to hold a coherent state where every observation is fragmented. It is an application of the core platform and adds no sensing of its own.

Read the architecture

Evidence ladder

1. E0 · Concept reached
2. E1 not reached
3. E2 not reached
4. E3 not reached
5. E4 not reached
6. E5 not reached

Project assurance panel

Mission and context

Keep a coherent airspace state close to the ground, where terrain, structures and clutter make every observation harder to interpret.

Primary contribution

A state that carries continuity through occlusion as a labelled inference, restores observation when a second viewpoint sees the object and widens its band while it is inferring.

Decision value

Lets a reviewer see exactly which parts of a track were observed and which were inferred, and how much trust the inferred parts deserve.

Evidence status

Evidence level: E0 · Concept Concept and research direction only. No specification, prototype, test result or deployment is claimed.

Next validation step

Write down how an inferred track is labelled and how its band widens, then specify a first review of a synthetic cutaway before any claim is made.

Behaviour when degraded

While every viewpoint is blocked, the track stays inferred and its band keeps widening, and after a limit it is dropped, not carried on. The state says which it did.

Role in integration

Would take viewpoints from distributed passive nodes, traces from optical and radio-frequency sensing, fused states from the fusion engine and conditions from atmospheric intelligence, and expose the result through the data API.

Human review

A person reviews every track that was inferred for any length of time before it is used for anything, and reads which viewpoints it rested on.

Public-safe limitation

No site, structure, terrain or sensor position is described. The cutaway on this page is a symbolic four-frame drawing.

Open unknowns

How long a track may be inferred, how much a second viewpoint should restore and how clutter should be weighed are not yet known.

While every viewpoint is blocked, the track stays inferred and its band keeps widening, and after a limit it is dropped, not carried on. The state says which it did.

• An area full of buildings, terrain or clutter

  Why it matters here

  A single view loses the object behind every obstacle, and the loss happens exactly where it matters.

  What it contributes

  A state that keeps going through the loss and says that it is inferring.

  What it does not replace

  Extra sensors where a site needs them, or the people who watch.

• A review of a track that disappeared

  Why it matters here

  Whether the track was lost, inferred or recovered changes what can be said about it.

  What it contributes

  A record in which observed and inferred stretches are marked apart.

  What it does not replace

  An investigation.

Would take viewpoints from distributed passive nodes, traces from optical and radio-frequency sensing, fused states from the fusion engine and conditions from atmospheric intelligence, and expose the result through the data API.

Write down how an inferred track is labelled and how its band widens, then specify a first review of a synthetic cutaway before any claim is made.

• This is a concept. There is no specification, prototype or measured result behind it.
• The cutaway uses four frames, two structures and an invented object. It shows how inference is marked, not how a system behaves.
• An inferred track is a hypothesis. The widening band shows that the trust it deserves falls with every frame.
• A second viewpoint does not always exist. Where it does not, the track stays inferred or is dropped.

• Drone Detection & Airspace Intrusion IntelligenceEvidence level: E0

  Low-altitude detection supplies the events whose tracks the cutaway keeps together.

• Distributed Passive Radar NetworkEvidence level: E0

  A distributed passive network supplies the complementary viewpoints.

• Multi-Sensor Fusion EngineEvidence level: E0

  Fusion combines the viewpoints and carries the inferred hypothesis.

• Airspace Intelligence Core PlatformEvidence level: E1

  The core platform holds the state, the history and the replay.

• Hyperlocal Atmospheric IntelligenceEvidence level: E0

  Atmospheric intelligence supplies the conditions that change what is visible.

• Airspace Data & Intelligence APIEvidence level: E0

  The data API is how observed and inferred stretches would reach other systems.