Airborne sonar target recognition using artificial neural network

Airborne sonar target recognition involves two key technical issues: target feature extraction and classification. In this paper, the issue designing a feature classifier with high classification accuracy is discussed. Generally, the multilayered feed-forward neural network can be applied to the airborne sonar target feature classification to achieve the high performance requirement. However, neural networks trained by conventional error back-propagation (B-P) learning algorithms suffer from slow convergence rate and inadequate generalization ability. Detailed analysis of the B-P algorithm reveals that these problems are mainly related to the magnitudes of the components of the gradient vector and the direction of the vector associated with the severely ill-conditioned nature of the Hessian matrix of the error function. A fast back-propagation (F-BP) algorithm is therefore developed to accelerate the learning speed of the B-P algorithm. A dynamic training strategy is then applied to the F-BP algorithm to improve the generalization ability. Experiments are carried out for airborne sonar target feature classification using these algorithms. The results show that the performance of the neural network classifier trained with the proposed algorithm is superior to that of traditional B-P algorithm with a seven-fold learning speed advantage over B-P.