State estimation for nonlinear state space models is a challenging task.
Existing assimilation methodologies predominantly assume Gaussian posteriors on
physical space, where true posteriors become inevitably non-Gaussian. We
propose Deep Bayesian Filtering (DBF) for data assimilation on nonlinear state
space models (SSMs). DBF constructs new latent variables $h_t$ on a new latent
(``fancy'') space and assimilates observations $o_t$. By (i) constraining the
state transition on fancy space to be linear and (ii) learning a Gaussian
inverse observation operator $q(h_t|o_t)$, posteriors always remain Gaussian
for DBF. Quite distinctively, the structured design of posteriors provides an
analytic formula for the recursive computation of posteriors without
accumulating Monte-Carlo sampling errors over time steps. DBF seeks the
Gaussian inverse observation operators $q(h_t|o_t)$ and other latent SSM
parameters (e.g., dynamics matrix) by maximizing the evidence lower bound.
Experiments show that DBF outperforms model-based approaches and latent
assimilation methods in various tasks and conditions.

提出了用于非线性状态空间模型的深度贝叶斯滤波（DBF）方法，通过构建新的潜在变量并利用高斯逆观测算子进行数据同化，使得 DBF 的后验分布始终保持高斯性质，克服了采样误差积累的问题，并在各种任务和条件下优于基于模型和潜在同化方法。

深度贝叶斯滤波在贝叶斯可信数据同化中的应用

Deep Bayesian Filter for Bayes-faithful Data Assimilation

Deep learning-based methods monopolize the latest research in the field of
thermal infrared (TIR) object tracking. However, relying solely on deep
learning models to obtain better tracking results requires carefully selecting
feature information that is beneficial to representing the target object and
designing a reasonable template update strategy, which undoubtedly increases
the difficulty of model design. Thus, recent TIR tracking methods face many
challenges in complex scenarios. This paper introduces a novel Deep Bayesian
Filtering (DBF) method to enhance TIR tracking in these challenging situations.
DBF is distinctive in its dual-model structure: the system and observation
models. The system model leverages motion data to estimate the potential
positions of the target object based on two-dimensional Brownian motion, thus
generating a prior probability. Following this, the observation model comes
into play upon capturing the TIR image. It serves as a classifier and employs
infrared information to ascertain the likelihood of these estimated positions,
creating a likelihood probability. According to the guidance of the two models,
the position of the target object can be determined, and the template can be
dynamically updated. Experimental analysis across several benchmark datasets
reveals that DBF achieves competitive performance, surpassing most existing TIR
tracking methods in complex scenarios.

在复杂情景中，本文介绍了一种新颖的深度贝叶斯滤波（DBF）方法，用于增强热红外（TIR）目标跟踪，通过利用运动数据来估计目标对象的潜在位置并动态更新模板，DBF 方法在多个基准数据集上取得了竞争性的性能，胜过大多数现有的 TIR 跟踪方法。