中文

Journal of Applied Sciences ›› 2011, Vol. 29 ›› Issue (6): 585-591.doi: 10.3969/j.issn.0255-8297.2011.06.006

• Signal and Information Processing • Previous Articles     Next Articles

Weighted Neighborhood Reconstruction Algorithm and Its Application to Acoustic Target Recognition

WANG Yi1;2, ZOU Ji-wei1, YANG Jun-an1;2, LIU Hui1;2, BAI Jing-lu3   

  1. 1. Department of Information, Electronic Engineering Institute, Hefei 230037, China
    2. Key Laboratory of Electronic Restriction, Anhui Province, Hefei 230037, China
    3. No.61541 Unit, Beijing 100094, China  
  • Received:2010-11-09 Revised:2011-02-28 Online:2011-11-30 Published:2011-11-28

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Abstract:

Abstract: Manifold learning methods are sensitive to noise especially in acoustic targets recognition. To deal with this problem, we present a novel manifold learning algorithm for noisy manifold, termed weighted neighborhood reconstruction (WNR). The algorithm builds a curve that can best reflect the trend of the noisy manifold sub-surface. The curve is extended to reconstruct the manifold sub-surface and calculate low dimensional embedding on the new surface. The proposed algorithm can minimize noise effects on manifold while keep the original surface trend. The algorithm is tested on public database and low attitude flying targets acoustic signal. Experiment results show that the proposed algorithm is robust against noise, and outperforms the other three methods cited in this paper.

Key words: acoustic targets recognition, noisy manifold learning, weighted neighborhood reconstruction

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