OrbitalWatch
Real-time space situational awareness dashboard that tracks 40,000+ orbiting objects, calculates collision risks, and simulates launch clearances on a 3D WebGL globe.
Overview
OrbitalWatch is a real-time Space Situational Awareness (SSA) dashboard that tracks 40,000+ orbiting objects, calculates collision risks, and evaluates launch trajectory clearances. Ingesting live TLE data from Celestrak and propagating positions with SGP4, it is designed as a free, open-source alternative to commercial space situational software that costs $10K–$100K+/year.
Problem Statement
Commercial SSA tools are priced out of reach for university labs, CubeSat operators, and independent debris researchers. Building a scalable tracking solution requires resolving heavy telemetry streaming and complex conjunction collision checks in real time without lagging the browser.
Project Context
Hackathon Project
Architecture
The platform uses three decoupled tiers: a python ingestion/propagation engine, a FastAPI + Socket.IO server, and a React Three Fiber frontend. The physics engine propagates satellite coordinates using SGP4 and publishes collision risks to Redis pub/sub. A FastAPI server handles socket connections to broadcast updates, and the React client runs CPU-intensive launch clearance simulation off-thread using Web Workers.
Technical Decisions
- →APScheduler Ingestion — schedules asynchronous Celestrak GP API fetches, storing records in PostgreSQL via SQLAlchemy for tracking updates.
- →React Three Fiber & InstancedMesh — renders thousands of satellites on a WebGL globe within a single draw call, maintaining a steady 60 FPS.
- →Redis Pub/Sub & WebSockets — streams 5-second updates to clients concurrently without blocking database processes.
- →Web Workers for Clearance Forecasts — delegates heavy 30-day forecast propagation to a background thread to prevent UI freezing.
Key Outcomes & Contributions
Built an open-source real-time Space Situational Awareness (SSA) platform using an APScheduler-driven Celestrak ingestion engine and SGP4 propagation algorithms, streaming telemetry at 60 FPS via Socket.IO.
Challenges
- !Conjunction Search Complexity — Naively checking O(N²·T) pairs for collisions was extremely CPU-intensive. Resolved by implementing a three-stage filter: a geocentric shell filter eliminating 90% of non-colliding pairs, a coarse 10-minute scan, and a fine 1-minute refinement.
- !Physics Library Integration — Aligning raw coordinate systems from SGP4 propagation algorithms (TEME coordinates) with Three.js 3D space.
- !Rendering Performance — Maintaining viewport fluid interaction under high coordinate data volumes using instanced rendering.
Learnings
- ✦Progressive filtering funnels are essential when dealing with O(N²) computational complexities in standard hardware.
- ✦Web Workers are critical for heavy computational logic in modern React applications to keep the main thread responsive.
- ✦Decoupling physics calculations from state presentation layers ensures backend scalability and cleaner frontend rendering loop code.