Hybrid Adaptive Kalman Filter
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Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification
arXiv:2606.02767v1 Announce Type: new Abstract: Kalman filtering performance is highly sensitive to model mismatch and noise covariance tuning. Learning-based approaches address these limitations but typically rely on supervised training with large datasets and do not produce consistent uncertainty estimates. In this paper, we propose a self-supervised Hybrid Adaptive Kalman Filter that learns structured corrections to system dynamics and process noise covariance from measurements alone...
FW-NKF: Frequency-Weighted Neural Kalman Filters
arXiv:2606.02251v1 Announce Type: new Abstract: 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...
Uncertainty-Aware Adaptive Sensor Fusion for Autonomous Navigation
Announce Type: new Abstract: This work introduces a hybrid deep learning approach integrated with an Unscented Kalman Filter (UKF) to enhance pose estimation accuracy in Visual-Inertial Odometry (VIO) for autonomous navigation. The proposed model employs a Vision Transformer (ViT) network to effectively capture temporal dependencies from inertial measurement unit (IMU) data and utilizes a Multiscale Convolutional Neural Network (MCNN) to learn optical flow-based motion cues from visual data....