An Opportunistic Positioning Algorithm for Internet of Vehicles Under Intermittent and GNSS-Degraded Environment

Abstract

The global navigation satellite system (GNSS) is commonly used for high-precision vehicle positioning by leveraging positioning satellites. However, this system faces challenges, such as occlusion and absorption. As a result, meeting the precise positioning requirements of fully autonomous vehicles becomes unattainable with GNSS alone. This article introduces a methodology that incorporates the use of signal of opportunity (SOP), inertial navigation system (INS), and map data to achieve reliable relative positioning in practical scenarios where reliance on the global navigation satellites falls short. The proposed algorithm uses opportunistic signals to correct INS errors for real-time positioning and integrates map data to accurately estimate the vehicle’s trajectory in space and time without relying on GNSS. A particle filter is employed to continuously estimate the vehicle’s position and velocity. Simulation and experimental results with ultra-wideband (UWB) SOPs are presented to evaluate the efficacy and accuracy of the proposed scheme in environments with intermittent GNSS degradation. The experimental outcomes show that the average relative positioning error of the proposed method is 0.2759 m, with the minimum and maximum relative errors being 0.0036 and 0.5446 m, respectively. Notably, the average relative positioning error is reduced by 86.9% and 31.9%, respectively, compared to GNSS/INS and UWB/INS positioning methods alone. To enhance the algorithm execution efficiency, this article also compares the average relative positioning error and execution efficiency under different particle numbers. Simulation results reveal that with 2000 particles, a tradeoff between the average relative positioning error and algorithm execution time can be achieved.