By fusing data from the Global Sensor Network (SGSN), Seradata, and other proprietary and public sources, the system detects deviations that may not be visible through traditional analysis alone.

AI Anomaly Detection:

AI Anomaly Detection runs on a scheduled cadence:

The Interest Factor is a relative measure of how unusual a satellite’s recent behavior is compared to its baseline.

Important:
The Interest Factor is comparative — it highlights objects that stand out relative to peers and historical behavior.

The primary visualization displays satellites plotted by NORAD ID and Interest Factor score.

You can refine results using:

The system highlights the top-ranked spacecraft based on Interest Factor.

For each object, you’ll see:

Selecting a spacecraft opens the Investigation View.

This view includes:

Each spacecraft includes detailed expandable analytics panels.

Tracks brightness changes over time, useful for detecting:

Displays proximity events that may indicate:

Shows how the Interest Factor evolved over time.

Helps determine:

Tracks orbital altitude changes.

Useful for identifying:

Highlights changes in angular behavior that may indicate:

For GEO satellites, additional metrics may include:

The comparison view is important because anomaly detection is not only about seeing a pattern — it is about seeing whether that pattern is unusual relative to the population. Comparison is what turns a chart from an interesting trend into a more meaningful behavioral signal.

Use this to:

Each investigation metric includes export options.

You can:

For effective use:

Consider deeper analysis if:

AI Anomaly Detection is powered by Slingshot’s AGATHA AI framework, enabling multi-source fusion and built-in explainability.

After identifying an anomaly, you can:

AI Anomaly Detection gives you advanced, explainable analytics to surface and investigate unusual satellite behavior with confidence.