AI for Mission Operations: A Software-First Reference Approach for Telemetry Anomaly Detection and Operator Decision Support
This white paper proposes a layered software architecture combining streaming telemetry ingest, hybrid ML-based anomaly detection, and an operator-facing decision-support layer for space mission operations.
Abstract
As the global space economy grows toward $1.8 trillion and the shift toward downstream services accelerates, mission operations teams face increasing data volumes and complexity. This paper presents a five-layer reference architecture that applies enterprise AI and systems engineering practices to space mission operations.
Reference Architecture
- L1 — Ground Adapter: Normalised telemetry ingestion across heterogeneous ground station protocols
- L2 — Stream Ingest: High-throughput, low-latency event streaming for real-time telemetry processing
- L3 — Feature Store: Time-series feature engineering and storage optimised for anomaly detection workloads
- L4 — Detection: Hybrid ML pipeline combining statistical baselines, supervised classifiers, and LLM-assisted triage
- L5 — Operator UI: Decision-support interface with contextual alerts, trend visualisation, and recommended actions
Security Model
Zero-trust architecture throughout: mTLS between all services, signed binary artifacts, software bill of materials (SBOM), and append-only cryptographic audit trails for all command and telemetry events.
Download & Enquiries
Contact UTZYx at info@utzyx.com to receive the full white paper, request a technical briefing, or discuss partnership opportunities in space technology.