The ensemble Gaussian mixture filter (EnGMF) is a powerful, convergent particle filter capable of medium-to-high dimensional non-linear filtering. The EnGMF relies on a resampling step that can generate physically unrealistic posterior samples, that would subsequently produce physically meaningless forecasts. This work introduces the discriminator-informed resampling procedure, that augments the posterior resampling step with a discriminator that accepts or rejects candidate particles based on their physical plausibility. In this work these discriminators are learned through a normalizing flow approach. Numerical experiments on both the Ikeda map and the Lorenz '63 system show that discriminator informed resampling procedure consistently reduces error relative to the standard EnGMF in low-ensemble regimes.

翻译：集成高斯混合滤波器是一种强大且收敛的粒子滤波器，能够处理中高维非线性滤波问题。该滤波器依赖于重采样步骤，但该步骤可能生成物理上不合理的后验样本，进而产生无物理意义的预测。本文提出了一种判别器引导的重采样过程，通过引入一个依据物理合理性接受或拒绝候选粒子的判别器来增强后验重采样步骤。本文中这些判别器采用归一化流方法进行学习。在Ikeda映射和Lorenz '63系统上的数值实验表明，在低集合规模下，与标准集成高斯混合滤波器相比，判别器引导的重采样过程能够持续降低误差。