Progress on AI Space IMU Drift Prediction + Looking for Collaborations

Hello everyone — I wanted to share an update on the project I posted about earlier.

We have now built a spacecraft navigation system that achieves a 63% improvement on real NASA datasets (validated on Astrobee data). This system integrates classical physics, quantum physics, and machine learning into a three-layer drift correction architecture for IMU sensors — not just AI and not just standard filters.

We are now developing a new filtering approach grounded in quantum mechanics, explicitly modeling multiple noise sources directly within the equations.

We have also secured a small grant and are advancing into real-world validation, including planned UAV and onboard tests to collect additional experimental data.

At this stage, we are looking for:

  • Collaborations with researchers, labs, and teams working in spacecraft navigation, sensing, and autonomy

  • Introductions to investors or domain leaders

  • Guidance/mentorship from experts in aerospace, navigation systems, and hardware testing

  • People who can offer insight or resources as we scale toward real-world deployment

I’m building this project from Turkey, where access to space infrastructure and missions is limited, so connections and collaboration make a huge difference.

If you or anyone you know might be open to a conversation, workshop, or introduction, I would truly appreciate it.

Thanks so much!
We need more people building the future, not just optimizing the present.

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