An association coefficient of a belief function and its application in a target recognition system

Objective To determine how changing and informing a user of the false alarm (FA) rate of an automated target recognition (ATR) system affects the user’s trust in and reliance on the system and their performance during an underwater mine detection task. Background ATR systems are designed to operate using a high sensitivity and a liberal decision criterion to reduce the risk of the ATR system missing a target. A high number of FAs in general may lead to a decrease in operator trust and reliance. Methods Participants viewed sonar images and were asked to identify mines in the images. They performed the task without ATR and with ATR at a lower and higher FA rate. The participants were split into two groups—one informed and one uninformed of the changed FA rate. Trust and/or confidence in detecting mines was measured after each block. Results When not informed of the FA rate, the FA rate had a significant effect on the participants’ response bias. Participants had greater trust in the system and a more consistent response bias when informed of the FA rate. Sensitivity and confidence were not influenced by disclosure of the FA rate but were significantly worse for the high FA rate condition compared with performance without the ATR. Conclusion and application Informing a user of the FA rate of automation may positively influence the level of trust in and reliance on the aid.

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Hierarchical Recognition System for Target Recognition from Sparse Representations

Mathematical Problems in Engineering ◽

10.1155/2015/527095 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 10

Author(s):

Zongyong Cui ◽

Zongjie Cao ◽

Jianyu Yang ◽

Hongliang Ren

Keyword(s):

Deep Learning ◽

Deep Structure ◽

Target Recognition ◽

Recognition System ◽

Automatic Target Recognition ◽

Feature Representation ◽

Small Data ◽

Learning Method ◽

Classification Problems ◽

Restricted Boltzmann Machines

A hierarchical recognition system (HRS) based on constrained Deep Belief Network (DBN) is proposed for SAR Automatic Target Recognition (SAR ATR). As a classical Deep Learning method, DBN has shown great performance on data reconstruction, big data mining, and classification. However, few works have been carried out to solve small data problems (like SAR ATR) by Deep Learning method. In HRS, the deep structure and pattern classifier are combined to solve small data classification problems. After building the DBN with multiple Restricted Boltzmann Machines (RBMs), hierarchical features can be obtained, and then they are fed to classifier directly. To obtain more natural sparse feature representation, the Constrained RBM (CRBM) is proposed with solving a generalized optimization problem. Three RBM variants,L1-RNM,L2-RBM, andL1/2-RBM, are presented and introduced to HRS in this paper. The experiments on MSTAR public dataset show that the performance of the proposed HRS with CRBM outperforms current pattern recognition methods in SAR ATR, like PCA + SVM, LDA + SVM, and NMF + SVM.

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