Self-supervised hybrid adaptive Kalman filter learns structured corrections for data-efficient joint tracking and classification.
Recursive KalmanNet: Deep Learning-Augmented Kalman Filtering for State Estimation with Consistent Uncertainty Quantification,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
FW-NKF embeds a causal spectral-shaping operator into the Kalman measurement residual and jointly learns observation and transition networks, reporting up to 10% lower localization error on chaotic systems and inertial pose estimation.
citing papers explorer
-
Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification
Self-supervised hybrid adaptive Kalman filter learns structured corrections for data-efficient joint tracking and classification.
-
FW-NKF: Frequency-Weighted Neural Kalman Filters
FW-NKF embeds a causal spectral-shaping operator into the Kalman measurement residual and jointly learns observation and transition networks, reporting up to 10% lower localization error on chaotic systems and inertial pose estimation.