Conic section function neural networks for sonar target classification and performance evaluation using ROC analysis

Abstract

The remote detection of undersea mines in shallow waters using active sonar is a crucial subject required to maintain the security of important harbors and cost line areas. Neural network classifiers have been widely used in classification of complex sonar signals due to its adaptive and parallel processing ability. In this paper, Conic Section Function Neural Networks (CSFNN) is used to solve the problem of classification underwater targets. Simulation results support the ability of CSFNN with computational advantages of traditional neural network structures to utilize highly complex sonar classification problem. Receiver Operating Characteristic (ROC) analysis has been applied to the neural classifier to evaluate the sensitivity and specificity of diagnostic procedures. The ROC curve of the classifier based on different threshold settings demonstrated excellent classification performance of the CSFNN classifier.