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Author Souza, Fren L. ♦ Nakamura, Eduardo F. ♦ Pazzi, Richard W.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2016
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Kalman filter ♦ Target tracking ♦ Particle filter
Abstract Target-tracking algorithms typically organize the network into a logical structure (e.g., tree, cluster, or faces) to enable data fusion and reduce communication costs. These algorithms often predict the target’s future position. In addition to using position forecasts for decision making, we can also use such information to save energy by activating only the set of sensors nearby the target’s trajectory. In this work, we survey of the state of the art of target-tracking techniques in sensor networks. We identify three different formulations for the target-tracking problem and classify the target-tracking algorithms based on common characteristics. Furthermore, for the sake of a better understanding of the target-tracking process, we organize this process in six components: target detection, node cooperation, position computation, future-position estimation, energy management, and target recovery. Each component has different solutions that affect the target-tracking performance.
Description Author Affiliation: Federal University of Western Para, Santarém-PA-Brazil (Souza, fren L.); University of Ontario Institute of Technology, Oshawa-ON-Canada (Pazzi, Richard W.); Federal University of Amazonas, Manaus-AM-Brazil (Nakamura, Eduardo F.)
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-06-01
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 49
Issue Number 2
Page Count 31
Starting Page 1
Ending Page 31


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Source: ACM Digital Library