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应用科学学报 ›› 2011, Vol. 29 ›› Issue (6): 585-591.doi: 10.3969/j.issn.0255-8297.2011.06.006

• 信号与信息处理 • 上一篇    下一篇

加权邻域重构及其在声目标识别中的应用

王一1;2， 邹继伟1， 杨俊安1;2， 刘辉1;2， 白京路3   

  1. 1. 电子工程学院信息系，合肥230037
    2. 安徽省电子制约技术重点实验室，合肥230037
    3. 361541部队，北京100094
  • 收稿日期:2010-11-09 修回日期:2011-02-28 出版日期:2011-11-30 发布日期:2011-11-28
  • 通信作者: 通信作者：杨俊安，教授，博导，研究方向：信号处理、智能计算等；E-mail: yangjunan@ustc.cn
  • 作者简介:通信作者：杨俊安，教授，博导，研究方向：信号处理、智能计算等；E-mail: yangjunan@ustc.cn
  • 基金资助:

    国家自然科学基金(No.60872113)资助

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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摘要：

摘要： 针对流形学习方法用于声目标识别时易受噪声干扰的情况，提出一种加权邻域重构算法，采用加权迭代方式构造出带噪流形子曲面中最能反映该曲面变化趋势的曲线，通过拓展该曲线对带噪流形子曲面进行重构，利用新曲面计算低维嵌入. 该算法在去除噪声的同时，最大限度地保持了原流形曲面的变化趋势，是一种适用于声目标识别的算法. 在公开数据库和低空飞行目标实际数据中进行实验，结果表明在识别正确率及运行时间上，本文提出的算法相对于其他3 种对比算法均取得了较好的效果.

关键词: 声目标识别, 噪声流形学习, 加权邻域重构

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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