Navigation research

Fiber Optic Gyroscope IMU Research

Role: FOG prototype build; sensor-fusion benchmarking
Period: 2023 — Present
Outcome: TODO(berk): a measurable result — e.g. bias stability achieved, drift over test duration.

FOG
IMU sensor fusion
Mahony filter
Madgwick filter

Developed as part of professional work. Architecture is described at a high level; proprietary schematics, source code and client details are omitted.

The problem

MEMS IMUs drift too much for some inertial navigation needs, but tactical or navigation-grade fiber optic gyroscopes (FOGs) are usually bought, not understood. This project builds a FOG prototype in-house to test IMU performance directly and to benchmark sensor-fusion algorithms against a higher-grade reference. TODO(berk): confirm and sharpen this framing — including whether this is Kayacı R&D or personal research.

My role

I built a FOG prototype for IMU testing, applying fiber-optics theory to an inertial measurement setup, and benchmarked sensor-fusion algorithms (Mahony, Madgwick) against it.

Constraints

TODO(berk): optical component budget, target bias stability, test duration and environment.

System architecture

TODO(berk): block diagram (SVG) of the FOG optical path and the electronics that turn the optical signal into a rate measurement.

Firmware design

TODO(berk): signal processing chain from photodetector to angular rate output, and how it feeds the sensor-fusion benchmark.

Communication

TODO(berk): how the FOG interfaces with the test/benchmark rig — serial, CAN — and why.

Hardest problem

TODO(berk): the specific optical or electronic problem — e.g. bias drift source-hunting, noise floor issues — how it was diagnosed and fixed.

Result

TODO(berk): measured bias stability / drift, and how Mahony vs Madgwick compared against the FOG reference.

What I’d do differently

TODO(berk)