Open AccessA novel approach to research on feature extraction of acoustictargets based on manifold learning
Author(s) -
Hui Liu,
Junan Yang,
Yi Wang
Publication year - 2011
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.60.074302
Subject(s) - robustness (evolution) , computer science , manifold alignment , nonlinear dimensionality reduction , feature extraction , manifold (fluid mechanics) , pattern recognition (psychology) , artificial intelligence , low altitude , altitude (triangle) , dimensionality reduction , mathematics , mechanical engineering , biochemistry , chemistry , geometry , engineering , gene
In order to overcome the deficiency of robustness of low altitude passive acoustic target recognition, the manifold learning is applied to the feature extraction of acoustic targets. Based on the classical algorithm of manifold learning, in the paper we study and discuss the low-dimensional manifold in the frequency-domain of acoustic signals. This method is used to solve the target recognition problem with two data sets to verify its effectiveness, after which the performance is analyzed. The result indicates that the manifold learning can discover the intrinsic feature and increase the accuracy and the robustness of low altitude passive acoustic target recognition system.