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 three parts:
polaris fetchwill download and normalize satellite telemetry from the SatNOGS network (or you can import your own).
polaris learnwill analyze the telemetry, produce a model of the connections between telemetry components, and save a dependency graph for visualization.
polaris vizis an interactive, browser-based 3D visualization of that dependency graph.