学习偏置：机器学习增强的粒子滤波器
Learning to Bias: Machine Learning-Enhanced Particle Filters
浏览论文内容
中文总结 AI 辅助
针对粒子滤波器样本效率低和维度扩展性差的问题，提出神经最优粒子滤波器（NOPFs），通过离线学习最优提议的摊销近似并即插即用，在不改变滤波目标下提升样本效率和分布准确性。
中文摘要 AI 辅助
序贯推断从含噪声且不完整的观测中估计潜在状态。粒子滤波器（PFs）是一类基于重要性采样的蒙特卡洛方法，为此任务提供了灵活的框架，但常因提议分布欠优而遭受样本效率低下和维度扩展性不佳的问题。我们通过将学习到的提议集成到PF框架中来应对这些挑战。我们引入了神经最优粒子滤波器（NOPFs），它从离线模拟的单步条件元组中学习最优提议的摊销近似。学习到的提议作为标准PF更新中的即插即用替代品，样本通过标准重要性权重进行校正，使得该方法在标准支撑和密度评估假设下渐近地针对相同的滤波分布。在推理复杂度各异的随机非线性基准上，NOPFs以适度的计算开销提高了样本效率和分布准确性，优于标准PF基线。该方法将数据驱动的提议学习融入经典推断，而不改变底层滤波目标。
英文摘要
Sequential inference estimates latent states from noisy and incomplete observations. Particle Filters (PFs), a class of Monte Carlo methods based on importance sampling, provide a flexible framework for this task, but often suffer from poor sample efficiency and unfavorable scaling with dimension, partly due to suboptimal proposal distributions. We address these challenges by integrating learned proposals into the PF framework. We introduce Neural Optimal Particle Filters (NOPFs), which learn an amortized approximation to the optimal proposal from offline simulated one-step conditioning tuples. The learned proposal is used as a drop-in replacement in standard PF updates, with samples corrected by standard importance weights so that the method asymptotically targets the same filtering distribution under standard support and density-evaluation assumptions. Across stochastic nonlinear benchmarks of varying inference complexity, NOPFs improve sample efficiency and distributional accuracy over standard PF baselines with modest computational overhead. The approach integrates data-driven proposal learning into classical inference without altering the underlying filtering objective.
发表机构
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
