applied research

Hybrid neural filtering for GNSS

Applied AI researcher · Torus

Applied research combining learned components with classical filtering and signal-processing methods for satellite positioning.

Satellite positioning combines noisy measurements with well-studied dynamical models. This project looks at hybrid approaches in which neural components are used together with classical filters rather than as a replacement for model-based methods.

The description is limited to the research problem and the methodological stance. Implementation details, metrics, and client information are omitted.