A new algorithm based on a Supervised Self-Organizing neural network for the pas sive sonar target recognition was proposed. Because of the incompleteness of the passive sonar exemplar set, the algorithm introduced a ...A new algorithm based on a Supervised Self-Organizing neural network for the pas sive sonar target recognition was proposed. Because of the incompleteness of the passive sonar exemplar set, the algorithm introduced a Multi-Activation-function structure and Supervised Self-Organizing competitive learning algorithm into the classic feed-forward neural networks,and obviously improved the generalization ability in target recognition. Besides, it can effi ciently reduce the learning time and avoid the local optimum. The recognition experiments of realistic passive sonar signals show that this new algorithm has good generalization ability and high recognition rate展开更多
In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(S...In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.展开更多
文摘A new algorithm based on a Supervised Self-Organizing neural network for the pas sive sonar target recognition was proposed. Because of the incompleteness of the passive sonar exemplar set, the algorithm introduced a Multi-Activation-function structure and Supervised Self-Organizing competitive learning algorithm into the classic feed-forward neural networks,and obviously improved the generalization ability in target recognition. Besides, it can effi ciently reduce the learning time and avoid the local optimum. The recognition experiments of realistic passive sonar signals show that this new algorithm has good generalization ability and high recognition rate
基金Projects(61471370,61401479)supported by the National Natural Science Foundation of China
文摘In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.
