For the complete machine-readable documentation index, see llms.txt.
← Back to projects

2022-07-15

AESA Radar ML Models

TensorFlow and PyTorch models for Eurofighter AESA radar digitization, focusing on denoising and pattern detection.

DefenseSignal ProcessingML

Outcome

Boosted real-time processing throughput by 20%.

Stack

TensorFlowPyTorchNumPyDockerGitLab CI

Collaboration

Worked with a 10+ person defense team to translate legacy radar processing into modern ML services that comply with security constraints.

Technical focus

  • Built denoising autoencoders for clutter reduction and anomaly detection.
  • Implemented pattern classifiers that flag maneuvers using sliding windows.
  • Containerized pipelines with GitLab CI for reproducible simulations.

Outcome

The upgraded stack accelerated digital twin experiments and improved operator trust with clear metrics for recall/precision during test flights.

Projects

Need this kind of system in your team?

I help teams ship document agents, RAG copilots, computer vision pipelines, and operational automations without the usual prototype-to-production gap.