Browsing by Subject "sonar"

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Open Access
Classification of target primitives with sonar using two non-parametric data fusion methods
(1996) Ayrulu, Birsel
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.
Open Access
Reliability measure assignment to sonar for robust target differentiation
(Elsevier, 2002) Ayrulu, B.; Barshan, B.
This article addresses the use of evidential reasoning and majority voting in multi-sensor decision making for target differentiation using sonar sensors. Classification of target primitives which constitute the basic building blocks of typical surfaces in uncluttered robot environments has been considered. Multiple sonar sensors placed at geographically different sensing sites make decisions about the target type based on their measurement patterns. Their decisions are combined to reach a group decision through Dempster-Shafer evidential reasoning and majority voting. The sensing nodes view the targets at different ranges and angles so that they have different degrees of reliability. Proper accounting for these different reliabilities has the potential to improve decision making compared to simple uniform treatment of the sensors. Consistency problems arising in majority voting are addressed with a view to achieving high classification performance. This is done by introducing preference ordering among the possible target types and assigning reliability measures (which essentially serve as weights) to each decision-making node based on the target range and azimuth estimates it makes and the belief values it assigns to possible target types. The results bring substantial improvement over evidential reasoning and simple majority voting by reducing the target misclassification rate. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.