Full
JIA Xianglong, postgraduate, majors in smartphone GNSS signal classification and environmental perception, E-mail: jiaxianglong@sjtu.edu.cn
Outline
Existing research on GNSS signal classification primarily targets professional-grade receivers, distinguishing between line-of-sight(LOS) and non-line-of-sight(NLOS) signals through the extraction of signal features to achieve high-accuracy positioning. However, the signal features and classification methods used in these studies may not be applicable to the signal characteristics of low-cost GNSS chips in smartphones. This paper proposes a machine learning-based method for classifying smartphone GNSS reception signals into LOS and NLOS. The method combines five key indicators: carrier-to-noise ratio, elevation angle, pseudorange residuals, normalized pseudorange residuals, and pseudorange residual percentage. It also integrates deep learning-based zenith view semantic segmentation and satellite calibration techniques to construct an accurate training dataset. Evaluations of various machine learning models in typical urban scenarios show that the proposed method achieves an average classification accuracy of 85.8% across different smartphone models.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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