Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings

Research Article

Improved RSA Localization Based on the Lagrange Multiplier Optimization

Download
120 downloads
  • @INPROCEEDINGS{10.1007/978-3-030-00557-3_63,
        author={Jiafei Fu and Jingyu Hua and Zhijiang Xu and Weidang Lu and Jiamin Li},
        title={Improved RSA Localization Based on the Lagrange Multiplier Optimization},
        proceedings={Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings},
        proceedings_a={MLICOM},
        year={2018},
        month={10},
        keywords={Wireless localization Non-line-of-sight error Quadratic programming Wireless sensor networks},
        doi={10.1007/978-3-030-00557-3_63}
    }
    
  • Jiafei Fu
    Jingyu Hua
    Zhijiang Xu
    Weidang Lu
    Jiamin Li
    Year: 2018
    Improved RSA Localization Based on the Lagrange Multiplier Optimization
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-00557-3_63
Jiafei Fu1, Jingyu Hua1,*, Zhijiang Xu1, Weidang Lu1, Jiamin Li2
  • 1: Zhejiang University of Technology
  • 2: Southeast University
*Contact email: eehjy@163.com

Abstract

The non-line-of-sight (NLOS) error in wireless network is the main factor that affects the accuracy of positioning algorithm. Therefore, this paper proposes an improved range-scaling-algorithm (RSA) using the Lagrange multiplier method in the wireless sensor networks, where we account for two kinds of nodes, i.e., the static nodes (SN) and the mobile nodes (MN). The key of the proposed algorithm is to construct a composite cost function by the Lagrange multiplier method. Meanwhile, the SN grouping operation followed by a positioning combination is proposed to further improve the performance. Simulation results show that the proposed algorithm can effectively suppress the loss of positioning accuracy caused by non-line-of-sight error. Moreover, the proposed algorithm performs better with increasing number of SNs.