NDLI: Evidential logical sensing using multiple sonars for the identification of target primitives in a mobile robot's environment

Content Provider	IEEE Xplore Digital Library
Author	Ayrulu, B. Barshan, B. Erkmen, I. Erken, A.
Copyright Year	1996
Description	Author affiliation: Dept. of Electr. Eng., Bilkent Univ., Ankara, Turkey (Ayrulu, B.)
Abstract	Physical models are used to model reflections from target primitives commonly encountered in mobile robot applications. These targets are differentiated by employing a multitransducer pulse/echo system which relies on both amplitude and time-of-flight (TOF) data in the feature fusion process, allowing more robust differentiation. Target features are generated as being evidentially tied to degrees of belief which are subsequently fused for multiple logical sonars at different geographical sites. This evidential approach helps to overcome the vulnerability of echo amplitude to noise and enables the modeling of nonparametric uncertainty. Feature data from multiple logical sensors are fused with Dempster-Shafer rule of combination to improve the performance of classification by reducing perception uncertainty. Using three sensing nodes, improvement in differentiation is between 20-40% without false decision, at the cost of additional computation. Simulation results are verified by experiments with a real sonar system. This evidential approach helps to overcome the vulnerability of the echo amplitude to noise and enables the modeling of nonparametric uncertainty in real time.
Starting Page	365
Ending Page	372
File Size	829741
Page Count	8
File Format	PDF
ISBN	078033700X
DOI	10.1109/MFI.1996.572202
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-12-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sonar Uncertainty Noise level Reflection Mobile robots Noise robustness Fusion power generation Sensor fusion Computational efficiency Computational modeling
Content Type	Text
Resource Type	Article

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