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一种基于加权直方图统计的矢量水听器垂直阵数据融合定向算法。

A Data Fusion Orientation Algorithm Based on the Weighted Histogram Statistics for Vector Hydrophone Vertical Array.

作者信息

Liang Yan, Meng Zhou, Chen Yu, Zhang Yichi, Wang Mingyang, Zhou Xin

机构信息

College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410073, China.

College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China.

出版信息

Sensors (Basel). 2020 Oct 1;20(19):5619. doi: 10.3390/s20195619.

DOI:10.3390/s20195619
PMID:33019593
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7582611/
Abstract

In this paper, we propose a data fusion algorithm based on the weighted histogram statistics (DF-WHS) to improve the performance of direction-of-arrival (DOA) estimation for the vector hydrophone vertical array (VHVA). The processing frequency band is firstly divided into multiple sub-bands, and the high-resolution multiple signal classification (MUSIC) algorithm is applied to estimate the azimuth of each sub-band for each vector hydrophone. Then, the weighted least square (WLS) data fusion technique is used to fuse the sub-band estimation results of multiple sensors. Finally, the weighted histogram statistics method is employed to obtain the synthesis results in the frequency domain. We carried out a simulation and sea trial of the 16-element VHVA to evaluate the performance of the proposed algorithm. Compared to several traditional processing algorithms, the beam width of the proposed approach is significantly narrower, the side lobes are considerably lower, and the mean square error (MSE) is effectively smaller. In addition, the DF-WHS method is more suitable to accurately estimate the target azimuth with a low signal-to-noise ratio (SNR) because the noise sub-band is suppressed in the weighted histogram statistics step. The DF-WHS method in this article provides a new approach to improve the performance of deep-sea target detection for the VHVA.

摘要

在本文中，我们提出了一种基于加权直方图统计的数据融合算法（DF-WHS），以提高矢量水听器垂直阵列（VHVA）的波达方向（DOA）估计性能。首先将处理频段划分为多个子频段，并应用高分辨率多重信号分类（MUSIC）算法对每个矢量水听器的每个子频段方位进行估计。然后，采用加权最小二乘（WLS）数据融合技术融合多个传感器的子频段估计结果。最后，运用加权直方图统计方法在频域中得到合成结果。我们对16元VHVA进行了仿真和海试，以评估所提算法的性能。与几种传统处理算法相比，所提方法的波束宽度明显更窄，旁瓣更低，均方误差（MSE）有效更小。此外，由于在加权直方图统计步骤中抑制了噪声子频段，DF-WHS方法更适合在低信噪比（SNR）情况下准确估计目标方位。本文中的DF-WHS方法为提高VHVA深海目标检测性能提供了一种新途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b0/7582611/b20bb83f85ce/sensors-20-05619-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b0/7582611/15bf9122d11f/sensors-20-05619-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b0/7582611/650819aaf642/sensors-20-05619-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b0/7582611/6486bbad3438/sensors-20-05619-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b0/7582611/3a2bddee2d8f/sensors-20-05619-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b0/7582611/1d33164e824c/sensors-20-05619-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b0/7582611/b20bb83f85ce/sensors-20-05619-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b0/7582611/15bf9122d11f/sensors-20-05619-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b0/7582611/650819aaf642/sensors-20-05619-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b0/7582611/6486bbad3438/sensors-20-05619-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b0/7582611/3a2bddee2d8f/sensors-20-05619-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b0/7582611/1d33164e824c/sensors-20-05619-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10b0/7582611/b20bb83f85ce/sensors-20-05619-g007.jpg

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本文引用的文献

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Tensor Approach to DOA Estimation of Coherent Signals with Electromagnetic Vector-Sensor Array.张量方法在电磁矢量传感器阵列相干信号波达方向估计中的应用。
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ESPRIT-Like Two-Dimensional DOA Estimation for Monostatic MIMO Radar with Electromagnetic Vector Received Sensors under the Condition of Gain and Phase Uncertainties and Mutual Coupling.
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Sensors (Basel). 2017 Oct 26;17(11):2457. doi: 10.3390/s17112457.
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A Low-Complexity ESPRIT-Based DOA Estimation Method for Co-Prime Linear Arrays.一种基于ESPRIT的低复杂度互质线性阵列波达方向估计方法。
Sensors (Basel). 2016 Aug 25;16(9):1367. doi: 10.3390/s16091367.