飞机起落架噪声源定位的压缩感知算法

宁方立; 张超; 潘峰; 刘勇; 韦娟

doi:10.7527/S1000-6893.2018.21810

航空学报 >

2018 , Vol. 39 >Issue 5: 121810 - 121810

DOI: https://doi.org/10.7527/S1000-6893.2018.21810

流体力学与飞行力学

飞机起落架噪声源定位的压缩感知算法

宁方立 ,
张超 ,
潘峰 ,
刘勇 ,
韦娟

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1. 西北工业大学机电学院, 西安 710072;
2. 西安电子科技大学通信工程学院, 西安 710071

收稿日期: 2017-10-18

修回日期: 2018-01-24

网络出版日期: 2018-01-24

基金资助

国家自然科学基金（51675425，51375385）；陕西省自然科学基础研究计划（2016JZ013）

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Compressive sensing algorithm for sound source location of aircraft landing gear

NING Fangli ,
ZHANG Chao ,
PAN Feng ,
LIU Yong ,
WEI Juan

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1. School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
2. School of Telecommunications Engineering, Xidian University, Xi'an 710071, China

Received date: 2017-10-18

Revised date: 2018-01-24

Online published: 2018-01-24

Supported by

National Natural Science Foundation of China (51675425,51375385); Natural Science Basic Research Plan in Shaanxi Province (2016JZ013)

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摘要

目前针对飞机起落架噪声源定位的研究方法主要是将麦克风阵列与波束成形算法相结合。常规波束成形（CBF）算法在计算时存在主瓣宽度过宽、结果易受旁瓣影响的问题。高级波束成形算法在计算时效率较差，有时会有违背物理现象的假声源出现。提出了一种将正交匹配追踪（OMP）算法与奇异值分解（SVD）相结合的起落架噪声源定位的OMP-SVD压缩感知算法。在消声实验室内进行飞机起落架噪声源定位试验，将OMP-SVD算法、CBF算法和OMP算法在不同频率下获得的结果进行对比。试验结果表明：①与OMP算法相比，OMP-SVD算法在不同频率下均能准确定位出起落架主声源；②与CBF算法相比，OMP-SVD算法显著提高了分辨率。

关键词： 麦克风阵列; 波束成形; 奇异值分解; 压缩感知; OMP-SVD算法

本文引用格式

宁方立 , 张超 , 潘峰 , 刘勇 , 韦娟 . 飞机起落架噪声源定位的压缩感知算法[J]. 航空学报, 2018 , 39(5) : 121810 -121810 . DOI: 10.7527/S1000-6893.2018.21810

Abstract

At present, source location of aircraft landing gear is investigated mainly by combining microphone arrays with beamforming algorithms. The Conventional BeamForming (CBF) method has the drawbacks that the main lobe is too wide and the computation result is susceptible to sidelobes. For the advanced beamforming algorithm, the computing time is too long, and false sound sources sometimes occur. This paper presents a new method, which combines the Orthogonal Matching Pursuit (OMP) algorithm with Singular Value Decomposition (SVD), to locate the noise source of the landing gear. Experiments are conducted in the anechoic chamber, the results obtained by three different methods at different frequencies are compared. The experimental results show that compared with the OMP algorithm, the OMP-SVD algorithm can locate the main sources of the landing gear at different frequencies accurately; compared with the CBF algorithm, the OMP-SVD algorithm can improve the resolution significantly.

Key words： microphone array; beamforming; singular value decomposition; compressive sensing; OMP-SVD algorithm

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