arXiv:2606.02767v1 Announce Type: cross 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 while preserving the probabilistic structure of the filter. This allows the innovation likelihood to be co
The paper addresses known limitations in Kalman filtering through hybrid, self-supervised learning, leveraging advancements in both classical control theory and modern AI techniques to improve data efficiency and uncertainty estimation.
Improved data-efficient tracking and classification methods are critical for real-world autonomous systems, reducing the need for massive datasets while maintaining robust performance in dynamic environments.
This research introduces a method for more robust and data-efficient state estimation in robotic and autonomous systems, potentially accelerating development and deployment by lowering data dependency for training.
- · Autonomous Robotics Companies
- · Logistics & Automation Sector
- · AI/ML Research Institutions
More reliable and adaptable autonomous systems in various applications.
Reduced development costs and faster iteration cycles for complex robotic systems due to less reliance on extensive labelled datasets.
Enhanced AI-driven decision-making in environments with limited sensor data or high uncertainty, further blurring lines between human and machine performance.
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Read at arXiv cs.LG