Aiming at the problem of localization error caused by Non-Line-Of-Sight (NLOS) in the digital key scenario of smart cars, a supervised learning-based classification model is constructed by analyzing the time-domain feature differences of the Channel Impact Response (CIR) dataset to identify the NLOS signals using a Convolutional Neural Network (CNN). Moreover the structure of the CNN network is optimized by considering the limitations of the vehicle-side hardware resources, and the effectiveness and robustness of the Ultra-Wideband (UWB) localization system are verified through real vehicle tests. The experimental results show that the accuracy of the UWB localization system incorporating deep learning and multi-modal localization algorithms is significantly improved compared with the localization trajectory without NLOS recognition processing.
| 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 |