Abstract:

As the most widely used low-cost GNSS terminals at present, smartphones are limited by the physical characteristics of their built-in linearly polarized antennas. Their GNSS signal reception is vulnerable to occlusion interference from complex environments such as urban buildings and trees, leading to significant issues in observations including prominent multipath effects and poor carrier phase continuity, which in turn cause a substantial degradation in positioning accuracy. To address this problem, this study takes multi-source observation data opened by the Android system as the core input, including GNSS raw measurement information, attitude yaw angle and velocity derived from inertial sensors, as well as quality indicators such as pseudorange residuals and position dilution of precision (PDOP) during positioning calculation. Dynamic data collection was conducted in typical complex urban scenarios, and a prediction and correction framework for the 3D real-time kinematic (RTK) positioning error of smartphones was constructed. Considering the spatiotemporal correlation and multi-feature coupling characteristics of positioning errors, this paper proposes a convolutional long short-term memory (CNN-LSTM) neural network integrated with a channel-spatial dual attention mechanism: the convolutional layer extracts the spatial correlation of multi-source features, the LSTM layer captures the temporal dependence of error sequences, and the dual attention mechanism enhances the weights of key satellite channels and core observation features respectively, thereby achieving accurate modeling of error patterns in complex environments. Based on tests conducted with two smartphones of different hardware configurations, the Xiaomi Mi 8 and Google Pixel 6, under asymmetric transition occlusion environments and severe occlusion environments, the results indicate that the positioning accuracy of the Mi 8 was improved by approximately 49.3% and 63.9%, respectively, while the Pixel 6 achieved improvements of 37.5% and 47.1%, respectively. These results verify the universality and effectiveness of the method across different hardware terminals and complex scenarios, providing a lightweight algorithmic support for high-precision positioning of smartphones.

Key words: multi-source GNSS observation data, smartphone RTK, convolutional long short-term memory (CNN-LSTM) network, positioning error prediction