FW-NKF: Frequency-Weighted Neural Kalman Filters
Robust state estimation is central to robotic autonomy, yet classical Kalman filters struggle with frequency-dependent disturbances and model mismatch such as sensor vibrations, electromagnetic interference, and periodic noise. Although Deep Kalman Filter (DKF) variants extend the Extended Kalman Filtering (EKF) framework by learning latent transitions, they lack explicit mechanisms to suppress band-limited noise components that typically corrupt sensor measurements in real-world scenarios. We introduce the Frequency-Weighted Neural Kalman Filter (FW-NKF), a unified hybrid approach that embeds a causal spectral-shaping operator into the Kalman measurement residual and jointly learns obser...
| Authors | Adnan Harun Dogan, Berken Utku Demirel, Christian Holz |
|---|---|
| Primary category | cs.RO |
| Published | 1 June 2026 |
| Last updated at source | 1 June 2026 |
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