Meng C.-X.,Harbin Institute of Technology |
Meng C.-X.,The Key Laboratory for Underwater Scientific Test and Control Technology |
Yang S.-E.,Harbin Institute of Technology |
Li G.-J.,The Key Laboratory for Underwater Scientific Test and Control Technology
Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology | Year: 2010
In order to obtain the spatial directivity of ship radiated noise at far field in shallow water, a new method is presented to compute the corresponding sound field distribution at far field. Firstly the sound pressure data measured by hydrophone array at near field was used to obtain the equivalent intensity and positions of multi-pole structure model for ship radiated noise, then the spatial directivity of far field radiated noise is computed by normal modes. A comparison between simulation results of far field spatial directivity of ship noise obtained by this method and the result obtained from direct computation using assumed data of sound sources show that the method presented in this paper can give an acceptable spatial directivity of far field.
Yang H.,Northwestern Polytechnical University |
Yang H.,The Key Laboratory for Underwater Scientific Test and Control Technology |
Dai J.,Northwestern Polytechnical University |
Sun J.,Northwestern Polytechnical University |
And 3 more authors.
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | Year: 2011
A novel adaptive immune multi-filed feature selection algorithm (AIFSA) for underwater acoustic tragets classification is proposed. The AIFSA is proposed to address the problem that the classification performance in classifying underwater acoustic targets declines as the dimension of feature set increases, and that classifying underwater acoustic targets is a small-sample-size classification problem. The AIFSA generates an initial population using prior knowledge, and then generates new generations through repetitive application of mutation, crossover, and adaptive immune operator. In each iteration, individuals with less number of features and with high classification accuracy are given higher fitness values. The advantages of AIFSA include: using of prior knowledge, fast convergence, and small size of optimal feature subset. The multi-field features are extracted from 4 classes of underwater targets and used in feature selection and classification extracted. Experimental results show that the AIFSA can select the subset of efficient features, and there is only a small decline in the accuracy of SVM classifier when the number of features is decreased about 60%. Compared with the genetic algorithm, the AIFSA is more stable and achieves better converge speed, and the feature subset obtained by AIFSA achieves better classification performance and generalizability.