TY - GEN
T1 - A Novel UWB Indoor Positioning Algorithm Based on SVM
AU - Hu, Zhengrui
AU - Zhao, Wanlong
AU - Wang, Shuangshuang
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.
PY - 2025
Y1 - 2025
N2 - Ultra-wideband (UWB) is considered as a mainstream positioning technology in the field of indoor positioning by virtue of its high-precision positioning, low power consumption and strong penetration. However, indoor positioning still faces a series of challenges, such as NLOS problem. NLOS environments are particularly common in indoor positioning, where signal reflections, refractions, and scattering between buildings and obstacles can lead to performance degradation of traditional positioning algorithms. Aiming at indoor WSN localization scenarios, a SVM-based UWB Indoor Positioning (SUIP) algorithm based on Support Vector Machine (SVM) is proposed, which is applied in a public dataset with different scenarios. In the model optimisation of SUIP, a parameter selection method of SVM is designed through cross-validation. It is experimentally verified that SUIP can reduce positioning error and maintain the robustness efficiently in three-dimension NLOS scenarios compared with classical Chan algorithm.
AB - Ultra-wideband (UWB) is considered as a mainstream positioning technology in the field of indoor positioning by virtue of its high-precision positioning, low power consumption and strong penetration. However, indoor positioning still faces a series of challenges, such as NLOS problem. NLOS environments are particularly common in indoor positioning, where signal reflections, refractions, and scattering between buildings and obstacles can lead to performance degradation of traditional positioning algorithms. Aiming at indoor WSN localization scenarios, a SVM-based UWB Indoor Positioning (SUIP) algorithm based on Support Vector Machine (SVM) is proposed, which is applied in a public dataset with different scenarios. In the model optimisation of SUIP, a parameter selection method of SVM is designed through cross-validation. It is experimentally verified that SUIP can reduce positioning error and maintain the robustness efficiently in three-dimension NLOS scenarios compared with classical Chan algorithm.
KW - Indoor Positioning
KW - Support Vector Machine
KW - UWB
UR - https://www.scopus.com/pages/publications/105002116514
U2 - 10.1007/978-3-031-86203-8_4
DO - 10.1007/978-3-031-86203-8_4
M3 - 会议稿件
AN - SCOPUS:105002116514
SN - 9783031862021
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 39
EP - 52
BT - Wireless and Satellite Systems - 14th EAI International Conference, WiSATS 2024, Proceedings
A2 - Chen, Hsiao-Hwa
A2 - Meng, Weixiao
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024
Y2 - 23 August 2024 through 25 August 2024
ER -