AI 中文总结
研究动态系统状态估计受多种因素挑战的问题，提出CASA-KalmanNet框架，集成CPDNet模块监测KalmanNet内部特征并提供可靠性指标，动态调节学习过程，实验表明其在模型不匹配时性能优越，接近最优经典方法。
AI 中文摘要
动态系统中的可靠状态估计常受到模型不匹配、未知噪声统计和时间变化的挑战。虽然像KalmanNet这样的人工智能辅助卡尔曼滤波器利用深度学习增强经典估计，但它们仍易受分布变化影响且缺乏自主适应机制。本文介绍了变化感知自适应KalmanNet（CASA-KalmanNet），这是一个在线适应框架。它集成了名为CPDNet的专用神经模块，以监测KalmanNet的可解释内部特征并提供可靠性下降的软指标。这些指标动态调节在线学习过程，无需来自变化状态的额外标签就能高效及时地适应系统中的突变和渐变。在线性和非线性状态空间模型上的数值实验表明，CASA-KalmanNet在模型不匹配时始终优于现有基于学习的滤波器，同时接近具有完整领域知识的最优经典方法的精度。
英文摘要
Reliable state estimation in dynamical systems is often challenged by model mismatches, unknown noise statistics, and temporal variations. While AI-aided Kalman filters such as KalmanNet leverage deep learning to enhance classical estimation, they remain vulnerable to distribution shifts and lack mechanisms for autonomous adaptation. This work introduces Change-Aware Self-Adaptive KalmanNet (CASA-KalmanNet), an online adaptation framework that integrates a dedicated neural module, termed CPDNet, to monitor the interpretable internal features of KalmanNet and provide soft indicators of reliability degradation. These indicators dynamically regulate an online learning process, enabling data-efficient and timely adaptation to both abrupt and gradual changes in the system without requiring additional state labels from the changed regime. Numerical experiments on linear and nonlinear state-space models show that CASA-KalmanNet consistently outperforms existing learning-based filters under model mismatch, while approaching the accuracy of optimal classical methods with full domain knowledge.
Comments15 pages, 11 figures
