在线识别部分可观测系统稀疏动态模型，精度提升超84%。
Sparse Kalman Identification for Partially Observable Systems via Adaptive Bayesian Learning
- 基于贝叶斯方法的自适应稀疏化，支持在线数据更新。
- 相比基线方法，模型识别准确率提升84.21%。
- 毫秒级响应速度，适合实时控制场景。
稀疏动力学识别是发现可解释物理模型并实现工程系统高效控制的关键工具。然而，现有方法依赖批量学习与完整历史数据，难以适用于序列化、部分可观测的实时场景。为此，本文提出一种在线稀疏卡尔曼识别（SKI）方法，融合增强型卡尔曼滤波（AKF）与自动相关性确定（ARD）。主要贡献包括：(1) 理论支撑的贝叶斯稀疏化方案，无缝嵌入AKF框架，适应在线序列数据；(2) 更新机制使卡尔曼后验反映基函数选择的动态变化，优化模型结构；(3) 显式梯度下降形式提升计算效率。实验表明，SKI方法在精确模型结构识别上达到毫秒级响应，真实世界实验中较基线AKF准确率提升84.21%。
原文摘要 · Abstract (English)
Sparse dynamics identification is an essential tool for discovering interpretable physical models and enabling efficient control in engineering systems. However, existing methods rely on batch learning with full historical data, limiting their applicability to real-time scenarios involving sequential and partially observable data. To overcome this limitation, this paper proposes an online Sparse Kalman Identification (SKI) method by integrating the Augmented Kalman Filter (AKF) and Automatic Relevance Determination (ARD). The main contributions are: (1) a theoretically grounded Bayesian sparsification scheme that is seamlessly integrated into the AKF framework and adapted to sequentially collected data in online scenarios; (2) an update mechanism that adapts the Kalman posterior to reflect the updated selection of the basis functions that define the model structure; (3) an explicit gradient-descent formulation that enhances computational efficiency. Consequently, the SKI method achieves accurate model structure selection with millisecond-level efficiency and higher identification accuracy, as demonstrated by extensive simulations and real-world experiments (showing an 84.21\% improvement in accuracy over the baseline AKF).
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