改进卡尔曼滤波噪声模型，让系统自适应动态变化环境。
Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators
- 用结构化参数建模噪声，实现动态自适应
- 在时变噪声环境下状态估计误差降低
- 适合需要实时调整的非平稳系统跟踪
基于嵌入式隐变量转移算子（ELTO）的卡尔曼滤波作为新型序列状态估计算法，其关键局限在于采用简化的噪声模型，难以动态适应非平稳过程。为此，本文提出一种基于ELTO的贝叶斯滤波方法，引入新的噪声模型结构化参数化方式，使噪声学习既可数据驱动地获得最优时不变模型，又能动态调整以响应非平稳过程中动态变化。实验表明，该结构化噪声自适应显著提升了在噪声干扰、时变环境下的状态估计性能。
原文摘要 · Abstract (English)
Kalman filters based on the Embedded Latent Transfer Operators (ELTO) emerge as novel statistical tools for sequential state estimation. However, a critical limitation stems from their use of simplified noise models, which fail to dynamically adapt to non-stationary processes. To address this limitation, we introduce an ELTO-based Bayesian filtering approach with a new structured parameterization for the filter's noise model. This parameterization enables structured noise adaptation, which couples the data-driven learning of an optimal time-invariant noise model with dynamic parameter adaptation that responds to changes in dynamics within non-stationary processes. Empirical results show that our structured noise adaptation improves the filter's dynamic state estimation performance in noisy, time-varying environments.
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