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The purpose of this paper is uncertainty evaluation in a target differentiation problem. In the problem ultrasonic data fusion is applied using Dezert–Smarandache theory (DSmT). Besides of presenting a scheme to target differentiation using ultrasonic sensors, the paper evaluates DSmT-based fused results in uncertainty point of view. The study obtains pattern of data for targets by a set of two ultrasonic sensors and applies a neural network as target classifier to these data to categorize the data of each sensor. Then the results are fused by DSmT to make final decision. The Generalized Aggregated Uncertainty measure named GAU2, as an extension to the Aggregated Uncertainty (AU), is applied to evaluate DSmT-based fused results. GAU2, rather than AU, is applicable to measure uncertainty in DSmT frameworks and can deal with continuous problems. Therefore GAU2 is an efficient measure to help decision maker to evaluate more accurate results and smoother decisions are made in final decisions by DSmT in comparison to DST.

The purpose of this paper is uncertainty evaluation in a target differentiation problem. In the problem ultrasonic data fusion is applied using Dezert-Smarandache theory (DSmT). Besides of presenting a scheme to target differentiation using ultrasonic sensors, the paper evaluates DSmTbased fused results in uncertainty point of view.

The purpose of this paper is uncertainty evaluation in a target  differentiation problem. In the problem ultrasonic data fusion is applied using Dezert-Smarandache theory (DSmT). Besides of presenting a scheme to target differentiation using ultrasonic sensors, the paper evaluates DSmT-based fused results in uncertainty point of view.

Abstract The purpose of this paper is uncertainty evaluation in a target differentiation problem. In the problem ultrasonic data fusion is applied using Dezert-Smarandache theory (DSmT). Besides of presenting a scheme to target differentiation using ultrasonic sensors, the paper evaluates DSmT-based fused results in uncertainty point of view. The study obtains pattern of data for targets by a set of two ultrasonic sensors and applies a neural network as target classifier to these data to categorize the data of each sensor. Then the results are fused by DSmT to make final decision. The Generalized Aggregated Uncertainty measure named GAU2, as an extension to the Aggregated Uncertainty (AU), is applied to evaluate DSmT-based fused results. GAU2, rather than AU, is applicable to measure uncertainty in DSmT frameworks and can deal with continuous problems. Therefore, GAU2 is an efficient measure to help decision maker to evaluate more accurate results and smoother decisions are made in final decisions by DSmT in comparison to DST.