# Online Particle Smoothing with Application to Map-matching

arXiv: https://arxiv.org/abs/2012.04602 · HTML: https://mlsanity.com/paper/2012.04602/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [A State-Space Perspective on Modelling and Inference for Online Skill Rating](https://mlsanity.com/paper/2308.02414/) (2023)

## Abstract (arXiv)

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.