带有低成本U-blox接收器的单频BDS/GPS RTK

Tianxia Liu, Bofeng Li
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引用次数: 17

摘要

低成本单频RTK(实时运动学)技术在GNSS的广泛应用中蓬勃发展,但低成本接收机的数据质量和硬件性能差,给其定位算法带来了很大的挑战。本文开发了一种BDS/GPS单频廉价导航定位(SINAP)系统,利用低成本GNSS接收机实现高精度、高稳定性的单频RTK定位。在数据处理中,我们实现了等速(CV)和等速(CA)动态模型下的卡尔曼滤波估计。由于在实际应用中无法确定整个整数模糊度,我们利用PAR(部分模糊度解决)方法来有效地获取整数模糊度的子集。采用低成本的单频u-blox接收机进行了静态和运动数据实验。为了强调所提出方法的优点,我们将SINAP的性能与开源软件RTKLIB和u-blox芯片的性能进行了比较。结果表明,SINAP的结果与u-blox的结果具有竞争力,并且优于RTKLIB的结果。此外,还研究了GPS-only、BDS-only和BDS/GPS组合的性能。结果表明,与仅使用GPS或BDS相比,使用BDS/GPS可以获得更高的固定率和更高的定位精度。此外,我们利用卡尔曼滤波,利用运动学数据对CA和CV动态模型进行了比较,结果表明两种模型都能提供比较理想的结果,没有明显差异。
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Single-frequency BDS/GPS RTK with low-cost U-blox receivers
Technology of low-cost single-frequency RTK (real time kinematic) is booming in popular GNSS application, however, the poor data quality and hardware performance of low-cost receivers bring about great challenges to its positioning algorithm. In this contribution, a BDS/GPS single-frequency inexpensive navigation and positioning (SINAP) system is developed to achieve high-precise high-stability single-frequency RTK positioning with low-cost GNSS receivers. In data processing, we implement the Kalman filter estimation with constant velocity (CV) and constant acceleration (CA) dynamic models. Since it is not feasible to fix the whole integer ambiguities in real applications, we utilize the PAR (partial ambiguity resolution) method to acquire a subset of integer ambiguities effectively. Experiments with static and kinematic data were carried out by using low-cost single-frequency u-blox receivers. To emphasize the benefits of the proposed method, we compare the performance of SINAP to those of the open source software RTKLIB and the u-blox chip. It is shown that results of SINAP are competitive to the ones of u-blox and outperform the ones of RTKLIB. Moreover, the performance of GPS-only, BDS-only and combination of BDS/GPS are investigated. The results reveal that using BDS/GPS can achieve higher fix-rate and higher positioning accuracy than using GPS or BDS only. Additionally, with Kalman filter, we compare the CA and the CV dynamic models using kinematic data, which illustrates that both models can provide comparatively ideal results with no obvious difference.
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