Academy Projects AIS Tracking & Anomaly Detection
AI / ML

AIS Tracking & Anomaly Detection

Receive AIS signals from maritime vessels and use on-board ML to detect anomalous behavior (dark shipping, route deviations). Downlink only flagged events to minimize bandwidth and support maritime domain awareness.

18-24 months Advanced 1U
1U
Form Factor
Advanced
Difficulty
18-24 months
Timeline
3
Disciplines

About This Project

Receive AIS signals from maritime vessels and use on-board ML to detect anomalous behavior (dark shipping, route deviations). Downlink only flagged events to minimize bandwidth and support maritime domain awareness.

Category: AI / ML

This is an advanced-level project with an estimated timeline of 18-24 months using a 1U form factor.

Overview

Every ocean-going vessel above a certain size is required to broadcast its identity, position, speed, and heading via the Automatic Identification System. These signals propagate well beyond the horizon, making them detectable from low Earth orbit. A space-based AIS receiver captures these maritime broadcasts across entire ocean basins, providing ship tracking data in regions where no coastal radar or terrestrial AIS station can reach. Adding an onboard machine learning layer transforms raw position reports into intelligence — detecting vessels that go dark by turning off their transponders, identifying unusual route deviations, and flagging suspicious behavioral patterns. Only flagged events are downlinked, reducing bandwidth requirements while focusing analyst attention on the most interesting activities. The project spans RF receiver design, antenna engineering, signal processing, machine learning, and maritime domain awareness — a genuinely interdisciplinary challenge. The main technical hurdle is the antenna: AIS operates at a frequency that requires a relatively long antenna element, which must deploy reliably after launch from a tightly packed configuration.

Technical Details

AIS receivers operate at 162 MHz — quarter-wave antenna is ~46 cm, requiring a deployable mechanism (measuring tape antenna with burn-wire release). Satlab Polaris 4-channel AIS receiver (~€10,000-15,000) is the standard CubeSat option but expensive. Alternative: RTL-SDR based receiver with custom firmware (lower sensitivity, higher risk). ML anomaly detection runs on ESP32-S3 co-processor using pre-trained model to flag dark shipping and route deviations. Downlink flagged events only.

Research & Notes

Aalborg University AAUSAT3 (2013) was the first student satellite to successfully operate an AIS receiver in space, collecting over 700,000 messages in 100 days. AIS at 162 MHz is substantially harder than ADS-B at 1090 MHz because the quarter-wave antenna is ~46 cm requiring deployable mechanism — one of the highest-risk CubeSat subsystems. AIS receivers are expensive (Satlab Polaris €10,000-15,000). Unless antenna deployment is already planned for another subsystem, ADS-B (project 15) is the recommended maritime/aviation tracking variant. Cost: $10,000-$16,000 for commercial receiver or $500-$2,000 for DIY SDR approach (with significantly reduced performance). Complexity: advanced.

Required Disciplines

This project spans 3 disciplines, making it suitable for interdisciplinary student teams.

CS
EE
Data Science

Next Steps

Ready to take on this project? Here's a general roadmap that applies to most CubeSat missions:

  1. Build your foundation: Complete the core modules in the CubeSat Academy to understand spacecraft subsystems, mission design, and development workflows.
  2. Form a team: Recruit students across the required disciplines and identify a faculty advisor. Plan for knowledge transfer between graduating and incoming members.
  3. Write a mission concept: Draft a 1–2 page document outlining your objectives, target orbit, payload requirements, and success criteria.
  4. Connect with a chapter: Join a Blackwing chapter for mentorship, shared resources, and access to the platform ecosystem.
  5. Explore the developer tools: Visit the Developer Portal for platform documentation, SDKs, and hardware specs.
  6. Plan your timeline: Map milestones to your academic calendar. Most projects align well with a 2–4 semester capstone or research sequence.
  7. Reach out: Contact us to discuss your project goals, platform selection, and path to orbit.

Ready to start this mission?

Connect with a Blackwing chapter for mentorship, platform access, and a path to orbit.

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