Research on autocorrelation and cross-correlation analyses in vehicular nodes positioning

Abstract

In recent years, the massive increase in car ownership has led to a dramatic increase of traffic accidents, especially in the case of multi-vehicle chain collisions. However, most researches of collision warning systems are focused on the single vehicle collision warning, because it is hard to get the accurate distance and location of the non-line of sight vehicle with the traditional ultrasonic or laser ranging methods. Nowadays, many intelligent transportation systems are based on global navigation satellite systems with the positioning accuracy of more than 10 m even in ideal environments. At the same time, global navigation satellite system often fails to operate in non-line of sight areas, such as forests, tunnels, or downtown. IEEE 802.11p is developed for vehicle-to-vehicle (V2V) communication in order to meet the requirement for high accuracy in high speed and multipath vehicle environments. In this article, we proposed an efficient time of arrival or ranging estimation method using IEEE 802.11p short preamble in order to mitigate the effect of multipath and low signal noise ratio. First, the time of arrival estimation is performed using autocorrelation and cross-correlation (auto-cross). And then, the approach to iterative update is presented to find the accurate time offset. Simulation results, in the international telecommunication union vehicle A channel and an additive white Gaussian noise channel, indicate that the proposed ranging method achieves superior accuracy over the traditional methods even in low signal noise ratio conditions and multipath environments.