Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings

Research Article

A Target Localization Algorithm for Wireless Sensor Network Based on Compressed Sensing

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  • @INPROCEEDINGS{10.1007/978-3-030-19086-6_54,
        author={Zhaoyue Zhang and Hongxu Tao and Yun Lin},
        title={A Target Localization Algorithm for Wireless Sensor Network Based on Compressed Sensing},
        proceedings={Advanced Hybrid Information Processing. Second EAI International Conference, ADHIP 2018, Yiyang, China, October 5-6, 2018, Proceedings},
        proceedings_a={ADHIP},
        year={2019},
        month={5},
        keywords={Compressed sensing Wireless Sensor Network QR-decomposition Localization},
        doi={10.1007/978-3-030-19086-6_54}
    }
    
  • Zhaoyue Zhang
    Hongxu Tao
    Yun Lin
    Year: 2019
    A Target Localization Algorithm for Wireless Sensor Network Based on Compressed Sensing
    ADHIP
    Springer
    DOI: 10.1007/978-3-030-19086-6_54
Zhaoyue Zhang1, Hongxu Tao2, Yun Lin2,*
  • 1: Civil Aviation University of China
  • 2: Harbin Engineering University
*Contact email: linyun_phd@hrbeu.edu.cn

Abstract

The sparse target location algorithm based on orth can solve the problem that the sampling dictionary does not satisfy the RIP property. Compared with the traditional method, the orth preprocessing can reduce the energy consumption and communication overhead, but the orth pretreatment will affect the sparsity of the original signal. So that the positioning accuracy is affected to a certain extent. In this paper, a sparse target location algorithm based on QR-decomposition is proposed. On the basis of orth algorithm, the sampling dictionary is decomposed by QR, which can’t change the sparsity of the original signal under the premise of satisfying the RIP property. The problem of sparse target location based on network is transformed into the problem of target location based on compressed perception, and the localization error is reduced. The experimental results show that the location performance of sparse target location algorithm based on QR-decomposition and centroid algorithm is much better than that the sparse target location algorithm based on orth, and the accuracy of target location is greatly improved.