Magsi Amjad Hussain, Díez Luis Enrique, Knauth Stefan
Faculty of Engineering, University of Deusto, Avda. Universidades 24, 48007 Bilbao, Spain.
Hochschule für Technik Stuttgart, Faculty of Computer Science, Geomatics and Mathematics, Schellingstraße 24, 70174 Stuttgart, Germany.
Micromachines (Basel). 2024 Sep 11;15(9):1141. doi: 10.3390/mi15091141.
The availability of raw Global Navigation Satellites System (GNSS) measurements in Android smartphones fosters advancements in high-precision positioning for mass-market devices. However, challenges like inconsistent pseudo-range and carrier phase observations, limited dual-frequency data integrity, and unidentified hardware biases on the receiver side prevent the ambiguity resolution of smartphone GNSS. Consequently, relying solely on GNSS for high-precision positioning may result in frequent cycle slips in complex conditions such as deep urban canyons, underpasses, forests, and indoor areas due to non-line-of-sight (NLOS) and multipath conditions. Inertial/GNSS fusion is the traditional common solution to tackle these challenges because of their complementary capabilities. For pedestrians and smartphones with low-cost inertial sensors, the usual architecture is Pedestrian Dead Reckoning (PDR)+ GNSS. In addition to this, different GNSS processing techniques like Precise Point Positioning (PPP) and Real-Time Kinematic (RTK) have also been integrated with INS. However, integration with PDR has been limited and only with Kalman Filter (KF) and its variants being the main fusion techniques. Recently, Factor Graph Optimization (FGO) has started to be used as a fusion technique due to its superior accuracy. To the best of our knowledge, on the one hand, no work has tested the fusion of GNSS Post-Processed Kinematics (PPK) and PDR on smartphones. And, on the other hand, the works that have evaluated the fusion of GNSS and PDR employing FGO have always performed it using the GNSS Single-Point Positioning (SPP) technique. Therefore, this work aims to combine the use of the GNSS PPK technique and the FGO fusion technique to evaluate the improvement in accuracy that can be obtained on a smartphone compared with the usual GNSS SPP and KF fusion strategies. We improved the Google Pixel 4 smartphone GNSS using Post-Processed Kinematics (PPK) with the open-source RTKLIB 2.4.3 software, then fused it with PDR via KF and FGO for comparison in offline mode. Our findings indicate that FGO-based PDR+GNSS-PPK improves accuracy by 22.5% compared with FGO-based PDR+GNSS-SPP, which shows smartphones obtain high-precision positioning with the implementation of GNSS-PPK via FGO.
安卓智能手机中原始全球导航卫星系统（GNSS）测量数据的可用性推动了面向大众市场设备的高精度定位技术的发展。然而，诸如伪距和载波相位观测不一致、双频数据完整性有限以及接收机端未识别的硬件偏差等挑战阻碍了智能手机GNSS的模糊度解算。因此，在诸如深城市峡谷、地下通道、森林和室内区域等复杂条件下，由于非视距（NLOS）和多径条件，仅依靠GNSS进行高精度定位可能会导致频繁的周跳。惯性/GNSS融合由于其互补能力，是解决这些挑战的传统常用方法。对于行人以及配备低成本惯性传感器的智能手机，常见的架构是行人航位推算（PDR）+GNSS。除此之外，不同的GNSS处理技术，如精密单点定位（PPP）和实时动态定位（RTK），也已与惯性导航系统（INS）集成。然而，与PDR的集成一直有限，并且只有卡尔曼滤波器（KF）及其变体是主要的融合技术。最近，因子图优化（FGO）由于其更高的精度开始被用作一种融合技术。据我们所知，一方面，尚无工作在智能手机上测试GNSS后处理运动学（PPK）与PDR的融合。另一方面，那些评估采用FGO的GNSS与PDR融合的工作一直是使用GNSS单点定位（SPP）技术进行的。因此，这项工作旨在结合使用GNSS PPK技术和FGO融合技术，以评估与常用的GNSS SPP和KF融合策略相比，在智能手机上可获得的精度提升。我们使用开源的RTKLIB 2.4.3软件，通过后处理运动学（PPK）改进了谷歌Pixel 4智能手机的GNSS，然后在离线模式下通过KF和FGO将其与PDR融合进行比较。我们的研究结果表明，基于FGO的PDR+GNSS-PPK与基于FGO的PDR+GNSS-SPP相比，精度提高了22.5%，这表明通过FGO实现GNSS-PPK后，智能手机可实现高精度定位。
