Abstract

A ground vehicle navigation approach in a global navigation satellite system (GNSS)-challenged environments is developed, which uses signals of opportunity (SOPs) in a closed-loop map-matching fashion. The proposed navigation approach employs a particle filter that estimates the ground vehicle’s state by fusing pseudoranges drawn from ambient SOP transmitters with road data stored in commercial maps. The problem considered assumes the ground vehicle to have a priori knowledge about its initial states as well as the position of SOPs. The proposed closed-loop approach estimates the vehicle’s states for subsequent time as it navigates without the GNSS signals. In this approach, a particle filter is employed to continuously estimate the vehicle’s position and velocity along with the clock error states of the vehicle-mounted receiver and SOP transmitters. The simulation and experimental results with cellular long-term evolution (LTE) SOPs are presented, evaluating the efficacy and accuracy of the proposed framework in different driving environments. The experimental results demonstrate a position root-mean-squared error (RMSE) of: 1.6 m over a 825-m trajectory in an urban environment with five cellular LTE SOPs, 3.9 m over a 1.5-km trajectory in a suburban environment with two cellular LTE SOPs, and 3.6 m over a 345-m trajectory in a challenging urban environment with two cellular LTE SOPs. It is demonstrated that incorporating the proposed map-matching algorithm reduced the position RMSE by 74.88%, 58.15%, and 46.18% in these three environments, respectively, from the RMSE obtained by an LTE-only navigation solution.