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计算机工程 ›› 2011, Vol. 37 ›› Issue (11): 267-268,277. doi: 10.3969/j.issn.1000-3428.2011.11.092

• 开发研究与设计技术 • 上一篇    下一篇

基于α分布的单矢量传感器多目标方位估计

董姝敏1,2,梁国龙1,付 进1   

  1. (1. 哈尔滨工程大学水声技术国防科技重点实验室,哈尔滨 150001;2. 吉林师范大学信息技术学院,吉林 四平 136000)
  • 收稿日期:2010-11-10 出版日期:2011-06-05 发布日期:2011-06-05
  • 作者简介:董姝敏(1978-),女,博士研究生,主研方向:盲信号分离,光电子信息技术,智能计算;梁国龙,教授、博士生导师;付 进,讲师、博士
  • 基金资助:
    哈尔滨工程大学基础研究基金资助项目(HEUFT08023)

Multi-source Direction Estimation Based on Alpha Distribution by Single Vector Sensor

DONG Shu-min  1,2, LIANG Guo-long  1, FU Jin  1   

  1. (1. National Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China; 2. College of Information Technology, Jilin Normal University, Siping 136000, China)
  • Received:2010-11-10 Online:2011-06-05 Published:2011-06-05

摘要: 研究 稳定分布条件下单矢量传感器的多目标方位估计问题。介绍多维 稳定分布特征函数的谱测度表示及谱测度估计方法,在此基础上推导出单矢量传感器输出速度正交分量满足特征函数谱测度表示方程,建立谱测度与目标方位间的对应关系,用特征函数法或一维投影法估计谱测度和目标源方位及数量。仿真结果表明,在 稳定分布假设下,基于谱测度的多目标方位估计法是合理有效的。

关键词: 单矢量水听器, 多目标方位估计, 稳定分布, 谱测度, 经验特征函数, 一维投影

Abstract: Multi-target direction estimation of a single vector sensor is studied mainly in the conditions of alpha stable distribution. Spectral measure presentation and spectral measure estimation method of multi-dimensional characteristic function of stable distribution are introduced. On this basis, it is derived that orthogonal components of output speed of single vector sensor meets spectral measure presentation equation of characteristic function, to establish the corresponding relationship between spectral measure and target direction estimation and to estimate spectral measure with the characteristic function method or the 1D projection method, then direction of arrival and the number of the target source are estimated. Simulation results show that in the alpha stable distribution assumption, multi-target direction estimation method is more reasonable and effective based on the spectral measure.

Key words: single vector hydrophone, multi-source direction estimation, alpha stable distribution, spectral measure, empirical characteristic function, 1D projection

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