Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification
A self-supervised Hybrid Adaptive Kalman Filter is proposed that learns structured corrections to system dynamics and noise covariance from measurements only, enabling probabilistic model classification via innovation likelihood. Experiments show improved estimation accuracy and robust classification in both low- and large-data regimes.
[2606.02767] Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification
[Submitted on 1 Jun 2026]
Title:Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification
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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 computed and subsequently used for model classification via generalized Bayesian inference. Experimental results on real-world and simulated datasets demonstrate improved estimation accuracy and statistical consistency as well as robust classification performance across both low-data and large-data scenarios.
Comments: 8 pages, 4 figures
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
ACM classes: I.2.9
Cite as: arXiv:2606.02767 [cs.RO]
(or arXiv:2606.02767v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.02767
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Rebecca Russell [view email] [v1] Mon, 1 Jun 2026 18:30:59 UTC (723 KB)
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