Ranking sensors using an adaptive fuzzy logic algorithm

Gil Shayer; Ofir Cohen; Ephraim Korach; Yael Edan

doi:10.1109/JSEN.2004.839596

Ranking sensors using an adaptive fuzzy logic algorithm

Gil Shayer, Ofir Cohen, Ephraim Korach, Yael Edan

Department of Industrial Engineering and Management

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

A procedure to rank sensors according to their noise rates was developed based on an adaptive fuzzy logic algorithm for sensor fusion. No a priori knowledge of the sensors performance is assumed. Simulation analysis indicated 83.33% successful ranking with noise rates up to 50%. In an indoor experiment with a mobile robot equipped with three logical sensors, 88% of the rankings were correct. The ranking procedure also indicates the ranking results success probability.

Original language	English
Pages (from-to)	59-66
Number of pages	8
Journal	IEEE Sensors Journal
Volume	5
Issue number	1
DOIs	https://doi.org/10.1109/JSEN.2004.839596
State	Published - 1 Feb 2005

Keywords

Adaptive algorithm
Fusion
Logical sensors
Performance measures

ASJC Scopus subject areas

Instrumentation
Electrical and Electronic Engineering

Access to Document

10.1109/JSEN.2004.839596

Cite this

@article{39a754bddc1b4bb49d93a4475e39a7fa,

title = "Ranking sensors using an adaptive fuzzy logic algorithm",

abstract = "A procedure to rank sensors according to their noise rates was developed based on an adaptive fuzzy logic algorithm for sensor fusion. No a priori knowledge of the sensors performance is assumed. Simulation analysis indicated 83.33% successful ranking with noise rates up to 50%. In an indoor experiment with a mobile robot equipped with three logical sensors, 88% of the rankings were correct. The ranking procedure also indicates the ranking results success probability.",

keywords = "Adaptive algorithm, Fusion, Logical sensors, Performance measures",

author = "Gil Shayer and Ofir Cohen and Ephraim Korach and Yael Edan",

note = "Funding Information: Manuscript received October 31, 2002; revised October 29, 2003. This work was supported in part by the Ministry of Defense MAFAT under Grant 2647 and in part by the Paul Ivanier Center for Robotics Research and Production Management, Ben-Gurion University of the Negev. The assosciate editor co-ordinating the review of this paper and approving it for publication was Prof. Fabien Josse.",

year = "2005",

month = feb,

day = "1",

doi = "10.1109/JSEN.2004.839596",

language = "English",

volume = "5",

pages = "59--66",

journal = "IEEE Sensors Journal",

issn = "1530-437X",

publisher = "Institute of Electrical and Electronics Engineers",

number = "1",

}

TY - JOUR

T1 - Ranking sensors using an adaptive fuzzy logic algorithm

AU - Shayer, Gil

AU - Cohen, Ofir

AU - Korach, Ephraim

AU - Edan, Yael

N1 - Funding Information: Manuscript received October 31, 2002; revised October 29, 2003. This work was supported in part by the Ministry of Defense MAFAT under Grant 2647 and in part by the Paul Ivanier Center for Robotics Research and Production Management, Ben-Gurion University of the Negev. The assosciate editor co-ordinating the review of this paper and approving it for publication was Prof. Fabien Josse.

PY - 2005/2/1

Y1 - 2005/2/1

N2 - A procedure to rank sensors according to their noise rates was developed based on an adaptive fuzzy logic algorithm for sensor fusion. No a priori knowledge of the sensors performance is assumed. Simulation analysis indicated 83.33% successful ranking with noise rates up to 50%. In an indoor experiment with a mobile robot equipped with three logical sensors, 88% of the rankings were correct. The ranking procedure also indicates the ranking results success probability.

AB - A procedure to rank sensors according to their noise rates was developed based on an adaptive fuzzy logic algorithm for sensor fusion. No a priori knowledge of the sensors performance is assumed. Simulation analysis indicated 83.33% successful ranking with noise rates up to 50%. In an indoor experiment with a mobile robot equipped with three logical sensors, 88% of the rankings were correct. The ranking procedure also indicates the ranking results success probability.

KW - Adaptive algorithm

KW - Fusion

KW - Logical sensors

KW - Performance measures

UR - http://www.scopus.com/inward/record.url?scp=13144293110&partnerID=8YFLogxK

U2 - 10.1109/JSEN.2004.839596

DO - 10.1109/JSEN.2004.839596

M3 - Article

AN - SCOPUS:13144293110

SN - 1530-437X

VL - 5

SP - 59

EP - 66

JO - IEEE Sensors Journal

JF - IEEE Sensors Journal

IS - 1

ER -

Ranking sensors using an adaptive fuzzy logic algorithm

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this