Research Article
Space Encoding Based Compressive Tracking with Wireless Fiber-Optic Sensors
@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
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.