论文标题
顺序蒙特卡洛的条件测量密度估计通过归一流的流动
Conditional Measurement Density Estimation in Sequential Monte Carlo via Normalizing Flow
论文作者
论文摘要
在连续蒙特卡洛方法的现实应用应用中，测量模型的调整具有挑战性。可区分粒子过滤器的最新进展导致了通过神经网络学习测量模型的各种努力。但是，在构建测量模型中，可区分粒子过滤器框架中的现有方法并不承认有效的概率密度，从而导致对状态信息的测量不确定性进行错误的量化。我们建议通过有条件地归一化的流量在测量模型中学习表达和有效的概率密度，以捕获给定状态的测量结果。我们表明，在视觉跟踪实验中，提出的方法可改善估计性能和更快的训练融合。
Tuning of measurement models is challenging in real-world applications of sequential Monte Carlo methods. Recent advances in differentiable particle filters have led to various efforts to learn measurement models through neural networks. But existing approaches in the differentiable particle filter framework do not admit valid probability densities in constructing measurement models, leading to incorrect quantification of the measurement uncertainty given state information. We propose to learn expressive and valid probability densities in measurement models through conditional normalizing flows, to capture the complex likelihood of measurements given states. We show that the proposed approach leads to improved estimation performance and faster training convergence in a visual tracking experiment.
