用神经网络改进粒子滤波，提升高维观测下的状态追踪能力
Deep Variational Sequential Monte Carlo for High-Dimensional Observations
- 用变分目标训练神经网络，自动学习粒子滤波的提议与转移分布
- 在部分高维观测下追踪洛伦兹吸引子，性能优于传统方法
- 适合需要精准后验估计的复杂动态系统建模任务
序列蒙特卡洛（SMC）或粒子滤波广泛应用于非线性状态空间系统，但其性能常受限于提议分布和状态转移分布的不佳近似。本文提出一种可微分粒子滤波器，利用无监督变分SMC目标，通过神经网络参数化提议与转移分布，以从高维观测中学习。实验结果表明，该方法在从高维且部分观测中追踪具有挑战性的洛伦兹吸引子时，优于现有基准模型。此外，基于证据下界（ELBO）的评估显示，该方法能更准确地表示后验分布。
原文摘要 · Abstract (English)
Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-transition distributions. This work introduces a differentiable particle filter that leverages the unsupervised variational SMC objective to parameterize the proposal and transition distributions with a neural network, designed to learn from high-dimensional observations. Experimental results demonstrate that our approach outperforms established baselines in tracking the challenging Lorenz attractor from high-dimensional and partial observations. Furthermore, an evidence lower bound based evaluation indicates that our method offers a more accurate representation of the posterior distribution.
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