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Ultra-Wideband Digital Key Localization System Based on Convolutional Neural Network
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Zhouxiang Ye1, Xinyang Wu1, Qiang Zhang2, Hongyin Shan2, Yun Chen1, 3
Automobile Technology | 2026, (6) : 30 - 37
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Automobile Technology | 2026, (6): 30-37
• Selected Academic Papers of the 28th CAST Annual Meeting •
Ultra-Wideband Digital Key Localization System Based on Convolutional Neural Network
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Zhouxiang Ye1, Xinyang Wu1, Qiang Zhang2, Hongyin Shan2, Yun Chen1, 3
Affiliations
  • 1.School of Microelectronics, Fudan University, Shanghai 200433
  • 2.Shanghai Ingeek Technology Co., Ltd., Shanghai 201210
  • 3.Optoelectronic Chip and Intelligent Network Innovation Center, Zhuhai Fudan Innovation Institute, Zhuhai 519000
Published: 2026-06-24 doi: 10.19620/j.cnki.1000-3703.20250339
Outline
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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.

Digital keys  /  Ultra-wideband technology  /  Convolutional neural networks (CNN)  /  Wireless localization
叶周翔, 吴昕阳, 张强, 单宏寅, 陈赟. 基于卷积神经网络的数字钥匙超宽带定位系统. 汽车技术, 2026 , (6) : 30 -37 . DOI: 10.19620/j.cnki.1000-3703.20250339
Zhouxiang Ye, Xinyang Wu, Qiang Zhang, Hongyin Shan, Yun Chen. Ultra-Wideband Digital Key Localization System Based on Convolutional Neural Network[J]. Automobile Technology, 2026 , (6) : 30 -37 . DOI: 10.19620/j.cnki.1000-3703.20250339
Year 2026 volume Issue 6
162
24
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doi: 10.19620/j.cnki.1000-3703.20250339
  • Online Date:2026-09-23
  • Published:2026-06-24
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  • Revised:2026-01-09
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Affiliations
    1.School of Microelectronics, Fudan University, Shanghai 200433
    2.Shanghai Ingeek Technology Co., Ltd., Shanghai 201210
    3.Optoelectronic Chip and Intelligent Network Innovation Center, Zhuhai Fudan Innovation Institute, Zhuhai 519000
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表12种不同金属材料的力学参数
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Family
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Number of
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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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