Title
Target identification with multiple logical sonars using evidential reasoning and simple majority voting
Abstract
In this study, physical models are used to model reflections from target primitives commonly encountered in a mobile robot's environment. These targets are differentiated by employing a multi-transducer pulse/echo system which relies on both amplitude and time-of-flight data, allowing more robust differentiation. Target features are generated as being evidentially tied to degrees of belief which are subsequently fused by employing multiple logical sonars at different geographical sites. Feature data from multiple logical sensors are fused with the Dempster-Shafer rule of combination to improve the performance of classification by reducing perception uncertainty. Dempster-Shafer fusion results are contrasted with the results of combination of sensor beliefs through a simple majority vote. The method is verified by experiments with a real sonar system. The evidential approach employed here helps to overcome the vulnerability of the echo amplitude to noise and enables the modeling of non-parametric uncertainty in real time.
Year
DOI
Venue
1997
10.1109/ROBOT.1997.619267
Proceedings of International Conference on Robotics and Automation
Keywords
Field
DocType
target identification,multiple logical sonars,evidential reasoning,simple majority voting,mobile robot's environment,multi-transducer pulse/echo system,time-of-flight data,amplitude data,robust differentiation,degrees of belief,Dempster-Shafer rule of combination,perception uncertainty,echo amplitude,nonparametric uncertainty
Control engineering,Sonar,Artificial intelligence,Majority rule,Case-based reasoning,Motion planning,Pattern recognition,Sensor fusion,Engineering,Evidential reasoning approach,Machine learning,Mobile robot,Feature data
Conference
Volume
Issue
ISSN
3
1
1050-4729
ISBN
Citations 
PageRank 
0-7803-3612-7
3
0.48
References 
Authors
5
3
Name
Order
Citations
PageRank
birsel ayrulu1766.54
Billur Barshan231327.83
Simukai W. Utete3303.27