A comparison of different approaches to target differentiation with sonar
Author(s)
Advisor
Barshan, BillurDate
2001Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
This study compares the performances of di erent classication schemes and fusion techniques
for target di erentiation and localization of commonly encountered features in indoor robot
environments using sonar sensing Di erentiation of such features is of interest for intelligent
systems in a variety of applications such as system control based on acoustic signal detection
and identication map building navigation obstacle avoidance and target tracking The
classication schemes employed include the target di erentiation algorithm developed by
Ayrulu and Barshan statistical pattern recognition techniques fuzzy c means clustering
algorithm and articial neural networks The fusion techniques used are Dempster Shafer
evidential reasoning and di erent voting schemes To solve the consistency problem arising in
simple ma jority voting di erent voting schemes including preference ordering and reliability
measures are proposed and veried experimentally To improve the performance of neural
network classiers di erent input signal representations two di erent training algorithms
and both modular and non modular network structures are considered The best classication
and localization scheme is found to be the neural network classier trained with the wavelet
transform of the sonar signals This method is applied to map building in mobile robot
environments Physically di erent sensors such as infrared sensors and structured light systems
besides sonar sensors are also considered to improve the performance in target classication
and localization.
Keywords
Sonar sensingtarget di erentiation
target localization
articial neural networks
learning
feature extraction
statistical pattern recognition
Dempster Shafer evidential reasoning
ma jority voting
sensing systems
acoustic signal processing
mobile robots
map building
Voronoi diagram