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基于MEMS矢量水听器阵列的混合源降维定位算法

Dimension Reduction Localization Algorithm of Mixed Sources Based on MEMS Vector Hydrophone Array.

作者信息

Shang Zhenzhen, Yang Libo, Zhang Wendong, Zhang Guojun, Zhang Xiaoyong, Kou Hairong

机构信息

Department of Intelligence and Automation, Taiyuan University, Taiyuan 030032, China.

State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan 030051, China.

出版信息

Micromachines (Basel). 2022 Apr 15;13(4):626. doi: 10.3390/mi13040626.

DOI:10.3390/mi13040626
PMID:35457929
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9026732/
Abstract

In this paper, a mixed sources dimension reduction Multiple Signal Classification (MUSIC) localization algorithm suitable for Micro-Electro-Mechanical System (MEMS) vector hydrophone linear arrays is proposed, which reduces the two-dimensional search to one-dimensional local search. Firstly, the Lagrangian function is constructed by quadratic optimization idea to obtain the estimates of azimuth angles. Secondly, the least square method is utilized for optimal match to obtain the direction-of-arrivals (DOAs) and ranges, and the range parameters are judged in Fresnel zone to obtain the azimuth information of all near-field sources. Finally, find the common DOAs and achieve high-resolution separation of far-field and near-field sources. Simulation and field experiments prove that the proposed algorithm only needs a small number of elements can solve the problem of port and starboard ambiguity, does not need to construct high-order cumulants or multi-dimensional search while the parameters are automatically matched with low computational complexity. This study provides an idea of the engineering application of vector hydrophone.

摘要

本文提出了一种适用于微机电系统（MEMS）矢量水听器线性阵列的混合源降维多重信号分类（MUSIC）定位算法，该算法将二维搜索简化为一维局部搜索。首先，通过二次优化思想构造拉格朗日函数以获得方位角估计值。其次，利用最小二乘法进行最优匹配以获得波达方向（DOA）和距离，并且在菲涅尔区内判断距离参数以获得所有近场源的方位信息。最后，找到公共DOA并实现远场和近场源的高分辨率分离。仿真和现场实验证明，所提算法仅需少量阵元即可解决左右舷模糊问题，无需构造高阶累积量或进行多维搜索，同时参数自动匹配且计算复杂度低。本研究为矢量水听器的工程应用提供了一种思路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/288231b4605d/micromachines-13-00626-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/3aa3d24259bc/micromachines-13-00626-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/f0d5f8f8d60d/micromachines-13-00626-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/0ed209058636/micromachines-13-00626-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/042446f40fda/micromachines-13-00626-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/45b4998f131b/micromachines-13-00626-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/e371d61e9489/micromachines-13-00626-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/d4ca85f8682a/micromachines-13-00626-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/2d857cd19fd9/micromachines-13-00626-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/19562f06832b/micromachines-13-00626-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/288231b4605d/micromachines-13-00626-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/3aa3d24259bc/micromachines-13-00626-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/f0d5f8f8d60d/micromachines-13-00626-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/0ed209058636/micromachines-13-00626-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/042446f40fda/micromachines-13-00626-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/45b4998f131b/micromachines-13-00626-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/e371d61e9489/micromachines-13-00626-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/d4ca85f8682a/micromachines-13-00626-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/2d857cd19fd9/micromachines-13-00626-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/19562f06832b/micromachines-13-00626-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dcab/9026732/288231b4605d/micromachines-13-00626-g010.jpg

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