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Improving Positioning Accuracy Using GPS Pseudorange Measurements for Cooperative Vehicular Localization

机译:使用GPS伪距测量技术进行协作车辆定位,提高定位精度

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摘要

Accurate positioning is a key factor for enabling innovative applications in intelligent transportation systems. Cutting-edge communication technologies make cooperative localization a promising approach for accurate vehicle positioning. In this paper, we first propose a ranging technique called weighted least squares double difference (WLS-DD), which is used to detect intervehicle distances based on the sharing of GPS pseudorange measurements and a weighted least squares method. It takes the carrier-to-noise ratio (CNR) of raw pseudorange measurements into consideration for mitigating noises so that it can improve the accuracy of the distance detection. We show the superiority of WLS-DD by conducting a series of field experiments. Based on intervehicle distance detection, we propose a distributed location estimate algorithm (DLEA) to improve the accuracy of vehicle positioning. The implementation of DLEA only relies on inaccurate GPS pseudorange measurements and the obtained intervehicle distances without using any reference points for positioning correction. Moreover, to evaluate the joint effect of WLS-DD and DLEA, we derive a data fitting model based on the observed distance detection bias from field experiments, which generates parameters in a variety of environments for performance evaluation. Finally, we demonstrate the effectiveness of the proposed solutions via a comprehensive simulation study.
机译:准确的定位是在智能交通系统中实现创新应用的关键因素。尖端的通信技术使协作式本地化成为准确定位车辆的有前途的方法。在本文中,我们首先提出了一种称为加权最小二乘双差(WLS-DD)的测距技术,该技术用于基于GPS伪距测量值的共享和加权最小二乘方法来检测车距。 。为了减少噪声,它考虑了原始伪距测量的载波噪声比(CNR),从而可以提高距离检测的准确性。通过进行一系列现场实验,我们展示了WLS-DD的优越性。基于车距检测,提出了一种分布式位置估计算法(DLEA),以提高车辆定位的准确性。 DLEA的实现仅依赖于不准确的GPS伪距测量和获得的车距,而无需使用任何参考点进行位置校正。此外,为了评估WLS-DD和DLEA的联合效果,我们基于实地实验中观察到的距离检测偏差推导了数据拟合模型,该模型在各种环境中生成用于性能评估的参数。最后,我们通过全面的仿真研究证明了所提出解决方案的有效性。

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