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Space Encoding Based Compressive Tracking with Wireless Fiber-Optic Sensors

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Machine Learning and Intelligent Communications (MLICOM 2017)

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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.

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Author information

Authors and Affiliations

  1. California State University, San Bernardino, CA, 92407, USA

    Qingquan Sun, Haiyan Qiao & Yunfei Hou

  2. University of Houston at Clear Lake, Houston, TX, 77058, USA

    Jiang Lu

  3. California State Polytechnic University, Pomona, Pomona, CA, 91768, USA

    Yu Sun

Authors
  1. Qingquan Sun
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  2. Jiang Lu
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  3. Yu Sun
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  4. Haiyan Qiao
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  5. Yunfei Hou
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Corresponding author

Correspondence to Qingquan Sun .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Xuemai Gu

  2. Harbin Institute of Technology, Weihai, Heilongjiang, China

    Gongliang Liu

  3. Shandong University, Weihai, Heilongjiang, China

    Bo Li

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Sun, Q., Lu, J., Sun, Y., Qiao, H., Hou, Y. (2018). Space Encoding Based Compressive Tracking with Wireless Fiber-Optic Sensors. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-73564-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73563-4

  • Online ISBN: 978-3-319-73564-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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