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Online Particle Smoothing with Application to Map-Matching

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Singh, S 

Abstract

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.

Description

Keywords

Smoothing methods, Approximation algorithms, State-space methods, Additives, Trajectory, Signal processing algorithms, Task analysis, State-space models, sequential Monte Carlo, particle smoothing, backward simulation, map-matching, GPS

Journal Title

IEEE Transactions on Signal Processing

Conference Name

Journal ISSN

1053-587X
1941-0476

Volume Title

Publisher

Institute of Electrical and Electronics Engineers (IEEE)
Sponsorship
EPSRC (1890282)
Engineering and Physical Sciences Research Council (1890282)