处理非高斯多模态观测数据？试试这篇的EnCF方法，比传统卡尔曼滤波灵活。
论文提出集成控制流滤波（EnCF）用于隐式数据同化，通过随机控制流和伴随匹配学习观测依赖控制。针对模拟器定义观测，EnCF-LF从样本学习替代条件能量。理论证明理想精确性并建立一步误差分解。数值实验显示，EnCF在非高斯、多对一、多模态和隐式观测模型上优于Kalman型滤波器。
Ensemble Controlled-Flow Filtering for Implicit Data Assimilation
Data assimilation estimates the state of a dynamical system from model forecasts and incoming observations. Many observation mechanisms, however, are many-to-one, implicit, non-smooth, or accessible only through simulation, and need not provide the residual structures or likelihood guidance required by existing ensemble filters. We introduce implicit data assimilation, in which the analysis law is defined as an energy tilt of the forecast distribution. We then propose the Ensemble Controlled-flow Filter (EnCF), which realizes this update through a stochastic controlled flow and learns the observation-dependent control by adjoint matching from terminal energy gradients. For simulator-defined observations, EnCF-LF learns a surrogate conditional energy from samples and applies the same controlled-flow solver. We prove ideal exactness, derive a one-step error decomposition, and establish non-accumulation of local errors under filter stability. Numerical results show that Kalman-type filters remain preferable for smooth additive-Gaussian observations, while the proposed methods are better suited to non-Gaussian, many-to-one, multimodal, and implicit observation models.