2020/12/31 by Samuel Duffield, Sumeetpal S. Singh · 8 citations
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Autonomous Vehicle Technology and Safety #Computer science #Computer vision #Context (archaeology) #Global Positioning System #Kalman filter #Map matching #Matching (statistics) #Mathematical optimization #Mathematics #Particle filter #Resampling #Smoothing #Statistics #Target Tracking and Data Fusion in Sensor Networks #Traffic Prediction and Management Techniques #Trajectory #stat.AP #stat.ME
paper · pdf · doi:10.1109/tsp.2022.3141259
published in IEEE Transactions on Signal Processing 70, 497-508 (Institute of Electrical and Electronics Engineers)
arxiv created 2021/08/02 · openalex publication_date 2022/01/01 · arxiv updated 2022/03/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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, calledparticle 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.