Real-time On and Off Road GPS Tracking

TL;DR

This paper presents a real-time GPS tracking model that accurately estimates vehicle positions and velocities on and off road networks, incorporating online learning of error parameters within a Bayesian framework.

Contribution

It adapts a conditionally linear tracking model to a Particle Learning framework, enabling online estimation of transition probabilities and errors in GPS tracking.

Findings

Performs well on a real city road network

Accurately estimates on/off road transition probabilities

Supports real-time, online learning of model parameters

Abstract

This document describes a GPS-based tracking model for position and velocity states on and off of a road network and it enables parallel, online learning of state-dependent parameters, such as GPS error, acceleration error, and road transition probabilities. More specifically, the conditionally linear tracking model of Ulmke and Koch (2006) is adapted to the Particle Learning framework of H. F. Lopes, et. al. (2011), which provides a foundation for further hierarchical Bayesian extensions. The filter is shown to perform well on a real city road network while sufficiently estimating on and off road transition probabilities. The model in this paper is also backed by an open-source Java project.