Achieving reliable intervehicle positioning is one of the most fundamental elements for many vehicular applications, including collision avoidance and autonomous driving. Vehicle position is generally provided by a global navigation satellite system (GNSS), which unfortunately suffers from inaccuracy to varying degrees in challenging environments, for example, GNSS outages. In this article, a reliable fusion technique, called non-Gaussian Redheffer weighted least squares ( nn GRWLSs), is proposed. This new approach highlights the intervehicle positioning estimation in multi-GNSS outage environments, such as complete, partial, and free GNSS pseudorange outages. The proposed method combines, on the one hand, the benefits of the Gaussian dynamical matrix principle and the Redheffer distribution function for the sparse property in complete GNSS pseudorange outages and, on the other hand, the use of the optimal window size to regulate the data flow generated by both the inertial navigation systems (INSs) and GNSS during a partial GNSS pseudorange outage. During the free GNSS pseudorange outage, the process ignores data from the INS, and instead, GNSS pseudorange information alone will be considered to compute the intervehicle positioning information. Consequently, weighted least squares is used as an intervehicle positioning estimator. To address the pseudorange uncommon and INS measurement noises, the generalized error distribution (GED) is used to estimate the non-Gaussian densities. Finally, road-test experiments are implemented to evaluate the consistency of the proposed approach. The experimental results show that the proposed nn GRWLS can accurately estimate the intervehicle positioning under various conditions (free, partial, and complete GNSS pseudorange outages).

中文翻译：

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多 GNSS 中断情况下基于 Redheffer 加权最小二乘模型实现可靠的车间定位


实现可靠的车辆间定位是许多车辆应用的最基本要素之一，包括防撞和自动驾驶。车辆位置通常由全球导航卫星系统 (GNSS) 提供，但不幸的是，在具有挑战性的环境中，例如 GNSS 中断，该系统会出现不同程度的不准确。在本文中，提出了一种可靠的融合技术，称为非高斯 Redheffer 加权最小二乘法（nn GRWLS）。这种新方法强调了多 GNSS 中断环境中的车辆间定位估计，例如完全、部分和免费 GNSS 伪距中断。该方法一方面结合了高斯动态矩阵原理和 Redheffer 分布函数在完全 GNSS 伪距中断中稀疏特性的优点，另一方面利用最佳窗口大小来调节数据在部分 GNSS 伪距中断期间，惯性导航系统 (INS) 和 GNSS 产生的流量。在免费 GNSS 伪距中断期间，该过程会忽略来自 INS 的数据，而是仅考虑 GNSS 伪距信息来计算车辆间定位信息。因此，加权最小二乘法被用作车辆间定位估计器。为了解决不常见的伪距和 INS 测量噪声，使用广义误差分布 (GED) 来估计非高斯密度。最后，进行道路测试实验来评估所提出方法的一致性。实验结果表明，所提出的 nn GRWLS 可以准确估计各种条件下（自由、部分和完全 GNSS 伪距中断）下的车辆间定位。