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| Dual terminal differential improved recognition algorithm for car PEPS system |
Kai LIU1( ),Xiao-jun JI1,*(),Zhong-hua ZHAO1,Yi-wen CAO1,Jian YANG2,Xiao-feng PANG2 |
1. School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2. Technology Center of SAIC Motor, Shanghai 201800, China |
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Abstract A differential K-nearest neighbor positioning algorithm based on dual terminals was designed aiming at the need for high-precision identification technology of Smartphone-based car passive entry and passive start (PEPS) system. The recognition results of the dual-terminal algorithm and the typical single-terminal algorithm were merged to improve the robustness and accuracy of the recognition algorithm through the improved Dempster-Shafer evidence theory. The accuracy of the internal and external state recognition of the terminal car was improved by 10% in the experimental scene by using the fusion algorithm. The error distance near the window was reduced from 20 cm to 5 cm in the vicinity of the window in which the conventional positioning algorithm was prone to misjudgment.
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Received: 28 September 2019
Published: 28 October 2020
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Corresponding Authors:
Xiao-jun JI
E-mail: 1148035619@sjtu.edu.cn;jxj127@sjtu.edu.cn
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用于轿车PEPS系统的双终端差分改进识别算法
针对基于智能手机的汽车无钥匙进入和启动系统（PEPS）车内外高精度辨识技术需求，设计基于双终端的差分K近邻定位算法. 通过改进的Dempster-Shafer证据理论，将双终端算法与典型单终端算法的辨识结果进行融合，提升识别算法的鲁棒性与准确性. 与传统的K近邻和概率分布法相比，融合算法在实验场景中对终端车内外状态的辨识准确率提升10%. 在传统定位算法易出现误判的车窗附近范围内，将误差距离从距车窗20 cm缩小到距车窗5 cm.
关键词：
无钥匙进入与启动系统(PEPS),
接收信号强度,
位置指纹,
K近邻法,
差分,
Dempster-Shafer证据理论
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