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Dual-frequency Collaborative Positioning for Minimization of GNSS Errors in Urban Canyons

机译：最小化城市峡谷中GNSS误差的双频协同定位

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Global Navigation Satellite Systems (GNSS) provide precise positioning under open-sky conditions, such as highways. However, in urban canyons, buildings block and reflect the signals, causing multipath positioning errors. Multi-frequency transmission and collaborative positioning are two technologies that have been proposed to reduce multipath errors. Still, the magnitude of their individual and combined advantage in reducing multipath errors is unknown. To fill this gap, we simulated dual-frequency collaborative positioning with four vehicles in an open-sky environment and in an urban environment. We compared two solution algorithms for position estimation: the Gauss-Newton solver (GN) and the extended Kalman filter (EKF). This paper presents the performance of these two algorithms under the previously mentioned assumptions. Furthermore, we show how the information from dual-frequency reception can be used to select the most relevant satellites. In the urban environment, the GN and the EKF using dual-frequency reception and collaborative positioning are the solutions with the smallest RMS positioning error (under 2.5 m), Additionally, in the simulated urban environment, dual-frequency reception contributes more to reducing multipath errors than collaborative positioning. As a consequence, when developing automotive positioning systems, multi-frequency reception and collaborative positioning should ideally be combined, but with higher priority on multi-frequency reception.
机译：全球导航卫星系统（GNSS）在开阔天空的条件下（例如高速公路）提供精确的定位。但是，在城市峡谷中，建筑物会阻挡并反射信号，从而导致多径定位错误。多频传输和协作定位是为减少多径误差而提出的两项技术。尽管如此，减少多径错误的个体优势和综合优势的大小仍然未知。为了填补这一空白，我们在开阔的天空环境和城市环境中模拟了四辆车的双频协同定位。我们比较了两种用于位置估计的解决方案算法：高斯牛顿求解器（GN）和扩展卡尔曼滤波器（EKF）。本文介绍了在前面提到的假设下这两种算法的性能。此外，我们展示了如何将来自双频接收的信息用于选择最相关的卫星。在城市环境中，使用双频接收和协同定位的GN和EKF是最小的RMS定位误差（在2.5 m以下）的解决方案。此外，在模拟的城市环境中，双频接收对减少多径做出了更大贡献错误比协作定位。因此，在开发汽车定位系统时，理想情况下应将多频接收和协作定位相结合，但在多频接收上具有更高的优先级。

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