基于空间变换预处理的噪声子空间投影法

陆典

doi:10.11999/JEIT230553

基于空间变换预处理的噪声子空间投影法

doi: 10.11999/JEIT230553

陆典^,

1.
哈尔滨工程大学水声技术全国重点实验室哈尔滨 150001
2.
教育部极地海洋声学与技术应用教育部重点实验室(哈尔滨工程大学) 哈尔滨 150001
3.
哈尔滨工程大学水声工程学院哈尔滨 150001

详细信息

作者简介:
陆典：男，博士生，研究方向为阵列信号处理、水下目标探测等

通讯作者:
陆典　ludian@hrbeu.edu.cn

中图分类号: TN919
计量
- 文章访问数: 330
- HTML全文浏览量: 283
- PDF下载量: 89
- 被引次数: 14
出版历程
- 收稿日期: 2023-06-06
- 修回日期: 2023-08-10
- 网络出版日期: 2023-08-17
- 刊出日期: 2023-12-26

A Noise Subspace Projection Method Based on Spatial Transformation Preprocessing

LU Dian^,

1.
National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory for Polar Acoustics and Application of Ministry of Education (Harbin Engineering University), Harbin 150001, China
3.
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China

摘要

摘要: 针对基于噪声子空间投影的空间谱合成技术对输入信噪比(SNR)要求较高的问题，该文提出一种基于空间变换预处理的改进噪声子空间投影法。在子阵维度上，对阵列数据进行拆分处理，得到空间-空间-频率3维数据；利用空间投影变换，将其重新投影为空间-频率2维数据，实现子阵维度相干累积，提高空间变换后输入信噪比；采用噪声子空间投影法，对变换后数据进行处理，实现空间谱合成。数值仿真和实测数据处理结果表明：相比噪声子空间投影法，在保持方位分辨率不变的前提下，所提方法对输入信噪比的最低要求降低约6 dB，有效提升了噪声子空间投影法的弱信源检测性能。
- 空间谱合成 /
- 噪声子空间投影 /
- 子阵维度 /
- 空间变换
Abstract: To address the issue of high input Signal-to-Noise Ratio (SNR) in the spatial spectrum synthesis technique based on noise subspace projection, an improved noise subspace projection method based on spatial transformation preprocessing is proposed. First, the receiver array is uniformly split into sub-arrays to form three-dimensional space-space-frequency data. Then, three-dimensional data is projected into two-dimensional space-frequency data by spatial transformation, realizing coherent accumulation in the sub-array dimension and enhancing the input SNR after spatial transformation. Finally, the spatial spectrum synthesis is achieved by processing the two-dimensional transformed data, based on the noise subspace projection method. Numerical simulation and data processing results demonstrate that, compared with the noise subspace projection method, the proposed method decreases effectively the minimum input SNR by 6 dB while maintaining the bearing resolution, enhancing effectively the weak target detection performance of the noise subspace projection method.
- Spatial spectrum synthesis /
- Noise subspace projection /
- Sub-array dimension /
- Spatial transformation

HTML全文

${\boldsymbol{X}}\left( {{w_l}} \right)$ 拆分示意图

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图 2 空间投影变换处理示意图

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图 3 2种方法输出空间谱(单信源)

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图 4 2种方法输出空间谱(等强度双信源)

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图 5 2种方法输出空间谱(不等强度双信源)

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图 6 2种方法正确检测信源概率

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图 7 2种方法方位估计均方根误差

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图 8 噪声子空间投影法对应时间方位历程图(a)

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图 9 所提方法对应时间方位历程图(a)

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图 10 噪声子空间投影法对应时间方位历程图(b)

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图 11 所提方法对应时间方位历程图(b)

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图 12 噪声子空间投影法对应时间方位历程图(c)

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图 13 所提方法对应时间方位历程图(c)

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图 14 噪声子空间投影法对应时间方位历程图

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图 15 所提方法对应时间方位历程图

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图 16 2种方法输出空间谱(t=200 s)

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表 1 数值仿真参数设置

参数类型	参数设置
阵型	水平线列阵
阵元数量M	32
阵元间距 $d$	2 m
采样频率 ${f_s}$	5 kHz
正横方位	0°
信源方位	${\theta _0}$
中心频率 ${f_0}$	375 Hz

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表 2 2种方法对应空间谱对比度评价

方法	边缘锐度	背景级(dB)
噪声子空间投影法	0.69	–21.56
所提方法	0.98	–48.67

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表 3 数据处理参数设置

参数类型	参数设置
阵型	水平线列阵
阵元数量M	64
阵元间距 $d$	4 m
采样频率 ${f_{\rm{s}}}$	5 kHz
正横方位 $\theta$	0°
未知信源方位 ${\theta _0}$	$- 70^\circ , - 50^\circ , - 30^\circ , - 10^\circ ,10^\circ ,30^\circ ,50^\circ ,70^\circ$
工作频段 ${f_{\rm{B}}}$	100～180 Hz

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