A Deep-Learning Based GNSS Scene Recognition Method for Detailed Urban Static Positioning Task via Low-Cost Receivers

Global Navigation Satellite Systems (GNSS)-based position service is widely applied in cities, but the precision varies significantly in different obstruction scenes. Scene recognition is critical for developing scene-adaptive GNSS algorithms. However, the complexity of urban environments and the unevenness of received signal especially in low-cost receivers limit the performance of GNSS-based scene recognition models. Therefore, our study aims to construct a scene recognition model suitable for urban static positioning and low-cost GNSS receivers. Firstly, we divide the scenes into five categories according to application requirements, including open area, high urban canyon, unilateral urban canyon, shade of tree and low urban canyon. We then construct feature vectors from original observation data and consider the geometric relationships between satellites and receivers. The different sensitivity to different scenes is discovered through an analysis of the performance of each feature vector in recognition. Therefore, a GNSS positioning scene recognition model based on multi-channel LSTM (MC-LSTM) is proposed. The results of experiments show that an accuracy of 99.14% can be achieved by our model. Meanwhile, only 0.75 s and 1.95 ms are required in model training per epoch and model prediction per data on a CPU, which presents a significant improvement of over 90% compared with existing works. Furthermore, our model can be transferred into different time periods quickly and can maintain robustness in situations where one or two types of observation data are missed. A maximum accuracy of 81.13% can be achieved when two channels are missed, while 96.06% is attainable when one channel is missed. Therefore, our model has the potential for real applications in complex urban environments.