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Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I

Research Article

Space Encoding Based Compressive Tracking with Wireless Fiber-Optic Sensors

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  • @INPROCEEDINGS{10.1007/978-3-319-73564-1_2,
        author={Qingquan Sun and Jiang Lu and Yu Sun and Haiyan Qiao and Yunfei Hou},
        title={Space Encoding Based Compressive Tracking with Wireless Fiber-Optic Sensors},
        proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I},
        proceedings_a={MLICOM},
        year={2018},
        month={2},
        keywords={Human tracking Multiplex sensing Compressive sensing Space encoding},
        doi={10.1007/978-3-319-73564-1_2}
    }
    
  • Qingquan Sun
    Jiang Lu
    Yu Sun
    Haiyan Qiao
    Yunfei Hou
    Year: 2018
    Space Encoding Based Compressive Tracking with Wireless Fiber-Optic Sensors
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-73564-1_2
Qingquan Sun1,*, Jiang Lu2,*, Yu Sun3,*, Haiyan Qiao1,*, Yunfei Hou1,*
  • 1: California State University
  • 2: University of Houston at Clear Lake
  • 3: California State Polytechnic University, Pomona
*Contact email: quanqian12345@gmail.com, luj@uhcl.edu, yusun@cpp.edu, hqiao@csusb.edu, yunfei.hou@csusb.edu

Abstract

This paper presents a distributed, compressive multiple target localization and tracking system based on wireless fiber-optic sensors. This research aims to develop a novel, efficient, low data-throughput multiple target tracking platform. The platform is developed based on three main technologies: (1) multiplex sensing, (2) space encoding and (3) compressive localization. Multiplex sensing is adopted to enhance sensing efficiency. Space encoding can convert the location information of multi-target into a set of codes. Compressive localization further reduces the number of sensors and data-throughput. In this work, a graphical model is employed to model the variables and parameters of this tracking system, and tracking is implemented through an Expectation-Maximization (EM) procedure. The results demonstrated that the proposed system is efficient in multi-target tracking.

Keywords
Human tracking Multiplex sensing Compressive sensing Space encoding
Published
2018-02-09
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-319-73564-1_2
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