电子学报 ›› 2014, Vol. 42 ›› Issue (6): 1041-1046.DOI: 10.3969/j.issn.0372-2112.2014.06.001

• 学术论文 •    下一篇

基于稀疏信号重构的近场源定位

梁国龙1, 韩博1, 林旺生1, 王丹2   

  1. 1. 哈尔滨工程大学水声技术重点实验室, 黑龙江哈尔滨 150001;
    2. 上海微小卫星工程中心, 上海 200050
  • 收稿日期:2012-12-22 修回日期:2013-04-01 出版日期:2014-06-25
    • 作者简介:
    • 梁国龙 男,1964年11月生于吉林省农安县.现为哈尔滨工程大学教授、博士生导师.主要从事水声定位与导航、水声对抗、水声目标探测等方面的科研工作.E-mail:liangguolong@hrbeu.edu.cn.;韩博 男,1986年7月出生于黑龙江省哈尔滨市.现为哈尔滨工程大学博士研究生,研究方向为阵列信号处理,水下定位与导航.E-mail:hanbo710@126.com.
    • 基金资助:
    • 国家自然科学基金 (No.51279043); 国家自然科学基金 (No.51209059); 水声技术国家级重点实验室基金 (No.9140C200203110C2003)

Near-Field Sources Localization Based on Sparse Signal Reconstruction

LIANG Guo-long1, HAN Bo1, LIN Wang-sheng1, WANG Dan2   

  1. 1. Science and Technolgy on Underwater Acoustic Laboratory, Harbin Engineering University, Harbin, Heilongjiang 150001, China;
    2. Shanghai Engineering Center for Micro-Satellite, Shanghai 200050, China
  • Received:2012-12-22 Revised:2013-04-01 Online:2014-06-25 Published:2014-06-25
    • Supported by:
    • National Natural Science Foundation of China (No.51279043); National Natural Science Foundation of China (No.51209059); National Key Laboratory of Underwater Acoustic Technology (No.9140C200203110C2003)

摘要:

针对近场源定位问题,提出了一种基于稀疏信号重构的定位方法.该方法通过约束稀疏信号的L1-范数求解优化问题,实现信源的定位.该方法采用一种新的方法约束噪声项系数以求解优化问题,无需噪声的先验知识.为了减小计算量,将近场源二维定位问题转化为两次一维参数估计.通过计算机仿真验证了该方法的性能.

关键词: 阵列信号处理, 近场, 源定位, 稀疏信号重构

Abstract:

Considering near-field sources localization, a method based on sparse signal reconstruction is presented. The sparse optimization problem is solved through minimizing L1-norm of sparse signals, and the sources localization is realized. The method uses an estimate of noise power as the tradeoff of the optimization problem, without prior knowledge on noise. To reduce the computational complexity, the two-dimensional positioning problem is transformed into two one-dimensional parameter estimation problems. Simulation results show the performances of the proposed method.

Key words: array signal processing, near-field, source localization, sparse representation

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