Sensors 2013, 13(12), 17193-17221; doi:10.3390/s131217193
Article

Sensor Reliability Evaluation Scheme for Target Classification Using Belief Function Theory

1,2,* email, 1email and 2,3email
Received: 28 October 2013; in revised form: 4 December 2013 / Accepted: 8 December 2013 / Published: 13 December 2013
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [1221 KB, updated 21 June 2014; original version uploaded 21 June 2014]
Abstract: In the target classification based on belief function theory, sensor reliability evaluation has two basic issues: reasonable dissimilarity measure among evidences, and adaptive combination of static and dynamic discounting. One solution to the two issues has been proposed here. Firstly, an improved dissimilarity measure based on dualistic exponential function has been designed. We assess the static reliability from a training set by the local decision of each sensor and the dissimilarity measure among evidences. The dynamic reliability factors are obtained from each test target using the dissimilarity measure between the output information of each sensor and the consensus. Secondly, an adaptive combination method of static and dynamic discounting has been introduced. We adopt Parzen-window to estimate the matching degree of current performance and static performance for the sensor. Through fuzzy theory, the fusion system can realize self-learning and self-adapting with the sensor performance changing. Experiments conducted on real databases demonstrate that our proposed scheme performs better in target classification under different target conditions compared with other methods.
Keywords: information fusion; sensor reliability; belief function theory; discounting factor; target classification
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Zhu, J.; Luo, Y.; Zhou, J. Sensor Reliability Evaluation Scheme for Target Classification Using Belief Function Theory. Sensors 2013, 13, 17193-17221.

AMA Style

Zhu J, Luo Y, Zhou J. Sensor Reliability Evaluation Scheme for Target Classification Using Belief Function Theory. Sensors. 2013; 13(12):17193-17221.

Chicago/Turabian Style

Zhu, Jing; Luo, Yupin; Zhou, Jianjun. 2013. "Sensor Reliability Evaluation Scheme for Target Classification Using Belief Function Theory." Sensors 13, no. 12: 17193-17221.


Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert