Classification of target primitives with sonar using two non-parametric data fusion methods
Author(s)
Advisor
Barshan, BillurDate
1996Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
140
views
views
73
downloads
downloads
Abstract
In this study, physical models are used to model reflections from target primitives
commordy encountered in a mobile robot’s environment. These tcirgets
are differentiated by employing a multi-transducer pulse/echo system which
relies on both cimplitude and time-of-flight (TOP) data in the feature fusion
process, cillowing more robust differentiation. Target features are generated
as being evidentially tied to degrees of belief which are subsequently fused
for multiple logical soncirs at different geographical sites. Feature data from
multiple logical sensors are fused with Dernpster-Shafer rule of combination to
improve the performance of classification by reducing perception uncertainty.
Using three sensing nodes, improvement in differentiation is 20% without false
decision, however, at the cost of additional computation. Simulation results
are verified by experiments with real sonar systems. As an alternative method,
neural networks are used for incorporating lecirning of identifying pcirameter
relations of target primitives. Amplitude and time-of-flight measurements of
.‘]1 sensor pairs cire fused with these neural networks. Improvement in differentiation
is 72% with 28% false decision at the cost of elapsed time until the
network learns these patterns. These two approaches help to overcome the vulnerability
of echo amplitude to noise and enable the modeling of non-parametric
uncertainty.
Keywords
ultrasonic transducerssonar
target classification
sensor-hased robotics
multi-sensor data fusion and integration
non-parametric data fusion
evidential reasoning
belief functions
Dempster-Shafer rmle of combination
logical sensing
artificial neural networks.