无需大量标注数据，就能同时精准追踪与分类目标。
Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification
- 从观测数据中自监督学习动态修正和噪声协方差
- 在低数据和大数据场景下均提升追踪精度与分类性能
- 保留滤波器的概率结构，支持可信度估计
卡尔曼滤波对模型失配和噪声协方差调参极为敏感。基于学习的方法虽能缓解此问题，但通常依赖大规模有监督训练，且无法提供一致的不确定性估计。本文提出一种自监督的混合自适应卡尔曼滤波器，仅通过观测数据学习系统动力学和过程噪声协方差的结构化修正，同时保持滤波器的概率结构。这使得可计算创新似然，并进一步通过广义贝叶斯推断实现模型分类。在真实世界和模拟数据集上的实验表明，该方法在估计精度、统计一致性以及跨低数据与大数据场景的分类鲁棒性方面均有显著提升。
原文摘要 · Abstract (English)
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.
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