[1]姬庆,程锦房,肖大为.基于矢量对角加载的近场目标定位方法[J].ag亚游ag8|官方,2019,41(03):49.[doi:.]
 JI Qing,CHENG Jinfang,XIAO Dawei.Near-field Target Localization Method Based on Vector Diagonal Loading[J].,2019,41(03):49.[doi:.]
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基于矢量对角加载的近场目标定位方法()
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《ag亚游ag8|官方》[ISSN:1008-1194/CN:61-1316/TJ]

卷:
41
期数:
2019年03
页码:
49
栏目:
出版日期:
2019-06-24

文章信息/Info

Title:
Near-field Target Localization Method Based on Vector Diagonal Loading
文章编号:
1008-1194(2019)03-0049-06
作者:
姬庆;?程锦房;?肖大为
海军工程大学兵器工程系,湖北 武汉430033
Author(s):
JI Qing;? CHENG Jinfang;? XIAO Dawei
Department of Weaponry Engineering, Naval University of Engineering, Wuhan 430033 China
关键词:
小快拍;?水雷战;?对角加载;?矢量水听器;?近场MVDR
Keywords:
small snapshots;? mine warfare;? diagonal loading;? vector hydrophone;? near-field MVDR
分类号:
TN911.7
DOI:
.
文献标志码:
A
摘要:
针对采样数据少的情况下近场最小方差无畸变(MVDR)算法定位性能差的问题,提出一种基于矢量对角加载的近场MVDR定位方法。首先推导了基于任意型矢量阵的近场MVDR算法,并分析了算法在快拍数较少时性能下降原因;矢量对角加载方法利用采样数据协方差矩阵的标准差作为加载量完成对角加载处理,获得改善的协方差矩阵;最后通过仿真和实验数据验证改进算法的有效性和稳定性。验证结果表明,提出的方法能克服小快拍数据的影响,提高水雷对近场区域目标的定位稳定性。
Abstract:
The performance of standard near MVDR (Minimum Variance Distortionless Response) algorithm would be decreased sharply when the sampled data is small. This paper proposed a near MVDR localization method based on vector diagonal loading. Firstly, the near MVDR algorithm based on arbitrary vector array was derived detailedly and the reasons for the performance degradation of the algorithm were analyzed when the snapshots number was small. Vector diagonal loading method used the standard deviation of sampling data covariance matrix as loading amount to complete diagonal loading processing and an improved covariance matrix was proposed. Finally, the effectiveness and robustness of the proposed algorithm was verified by simulation and experiment data. Simulation results revealed that the proposed algorithm could overcome the influence of small snapshots and improved the stability of near-field target location.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-01-15
作者简介:姬庆(1991—),男,安徽淮北人,博士研究生,研究方向:军用目标特性及信息感知技术。E-mail:jiumingya886@163.com。
更新日期/Last Update: 2019-07-11