Authors: M. Khodabandeh, A. Mohammad-Shahri
The purpose of this paper is uncertainty evaluation in a target differentiation problem. In the problem ultrasonic data fusion is applied using Dezert-Smarandache theory (DSmT). Besides of presenting a scheme to target differentiation using ultrasonic sensors, the paper evaluates DSmTbased fused results in uncertainty point of view. The study obtains pattern of data for targets by a set of two ultrasonic sensors and applies a neural network as target classifier to these data to categorize the data of each sensor. Then the results are fused by DSmT to make final decision. The Generalized Aggregated Uncertainty measure named GAU2, as an extension to the Aggregated Uncertainty (AU), is applied to evaluate DSmT-based fused results. GAU2, rather than AU, is applicable to measure uncertainty in DSmT frameworks and can deal with continuous problems. Therefore GAU2 is an efficient measure to help decision maker to evaluate more accurate results and smoother decisions are made in final decisions by DSmT in comparison to DST.
Comments: 11 Pages.
Download: PDF
[v1] 2014-12-02 00:36:39
Unique-IP document downloads: 62 times
Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.
Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.