Overview

The aim of the Polaris project is to analyze satellite telemetry in order to understand links and dependencies among different subsystems, and between the spacecraft and its context.

It produces a data-driven analysis that should be able to demonstrate understanding of the links between the different behaviour changes of each telemetry within a satellite, or within a set of external sources of information (mission plan, solar aspect angles, ephemerides, etc.).

Machine learning is used to learn the dependencies and correlations happening within a spacecraft. The acquired knowledge is stored in a dependency graph (e.g. Bayesian network) – both for analysis, and to allow operators to examine future changes by comparison against older versions of the graph.

Polaris is split into four parts:

• polaris fetch will download and normalize satellite telemetry from the SatNOGS network (or you can import your own).

• polaris learn will analyze the telemetry, produce a model of the connections between telemetry components, and save a dependency graph for visualization.

• polaris viz is an interactive, browser-based 3D visualization of that dependency graph.

• polaris convert will convert graph output from polaris learn to another file format (like .gexf).