A Vector Tracking Loop Based on ML Estimation in Dynamic Weak Signal Environments

Abstract

Vehicle positioning with GNSS in urban environments faces two problems which are attenuation and dynamic. In the tracking loops of traditional GNSS receivers, using non-coherent discriminators, thermal noise error and dynamic stress threshold are contradictory. Thus, it is difficult for the traditional tracking loops using non-coherent discriminators to track weak signals in dynamic environments. In order to improve the tracking sensitivity of GNSS receiver in dynamic weak signal environments, a Joint Vector Position Tracking Loop (JVPTL) is proposed in the paper, in which the design of discriminator is based on Maximum Likelihood Estimation (MLE) according to the linear model of received baseband signal. Thus, the discriminator in the JVPTL is a coherent discriminator, which has relatively small discriminating errors and is able to ease the contradiction between thermal noise error and dynamic stress threshold. Moreover, the JVPTL tracks user position and velocity directly and takes full advantage of the coupling between codes and carriers of all visible satellite signals to reduce thermal noise error. Therefore, the proposed JVPTL is able to track dynamic weak signal. Simulation results show that in dynamic weak signal environments, the JVPTL performs better than the VDFLL with non-coherent discriminators.