论文标题
在线粒子平滑，并应用地图匹配
Online Particle Smoothing with Application to Map-matching
论文作者
论文摘要
我们介绍了一种新颖的方法，用于在状态空间模型中使用固定lag近似来克服众所周知的路径退化问题。与仅近似某些边缘的经典固定延迟技术不同，我们引入了一种在线重采样算法，称为粒子缝制，将这些边缘样品转换为完整的后近似值。我们在地图匹配的背景下演示了我们方法的实用性，即在道路网络和嘈杂的GPS观测下推断车辆轨迹的任务。我们开发了一种新的州空间模型，用于在密集的城市道路网络上匹配匹配的艰巨任务。
We introduce a novel method for online smoothing in state-space models that utilises a fixed-lag approximation to overcome the well known issue of path degeneracy. Unlike classical fixed-lag techniques that only approximate certain marginals, we introduce an online resampling algorithm, called particle stitching, that converts these marginal samples into a full posterior approximation. We demonstrate the utility of our method in the context of map-matching, the task of inferring a vehicle's trajectory given a road network and noisy GPS observations. We develop a new state-space model for the difficult task of map-matching on dense, urban road networks.
