基于联合信息处理的声矢量阵测向技术
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摘要
声矢量传感器由无指向性声压传感器和具有偶极子指向性的质点振速传感器复合而成，可以空间共点同步拾取声场中的标量信息(声压)和矢量信息(质点振速)。因此，与传统的声压传感器相比，声矢量传感器可以获取更为全面的声场信息。它的出现，为解决水下目标的检测、定位及噪声识别等诸多问题提供了一种新的方式和手段，深受国内外学者关注。目前，关于声矢量传感器的物理基础和工程制作等方面的问题已基本解决，基于声矢量传感器阵列(或简称声矢量阵)的信号处理问题已成为另一个令人瞩目的研究热点，一些成果相继被发表。但是，现有的声矢量阵信号处理技术都是将声矢量传感器的振速分量作为与声压相同的独立阵元信息来处理，而没有充分利用声压振速联合信息处理技术。事实上，在远程声场，相干源(尺度有限的信号源)信号的声压和振速是相干的，而对于各向同性噪声场，声压与振速是不相关的，所以基于平均声能流概念(即声强)的声压振速联合信息处理技术具有较强的抗各向同性噪声能力。基于单矢量传感器的声压振速联合信息处理技术已经为学者们广泛认可。
与此同时，在DOA估计的诸多方法中，MUSIC、ESPRIT等基于特征分解理论的子空间类方法，以其较高的分辨能力和相对较小的计算复杂度而颇受关注，近来，已有学者将其引入到声矢量阵信号处理中，并取得了一定的效果。
本文在回顾声矢量传感器的基本原理、声压振速联合信息处理的物理基础以及基于子空间类方法的声矢量阵信号处理技术研究现状的基础上，着眼于将声压振速联合信息处理技术引入到声矢量传感器阵列信号处理中。通过将联合信息处理技术与子空间类高分辨测向技术相结合，为解决声矢量阵在低信噪比条件下的信源检测、目标定位和噪声测量等方面的应用问题，提供一种新的思路和方法。本文的主要创新点如下：
1) 在对声矢量传感器的基本原理以及声压振速联合信息处理的物理基础进行了深入分析的基础上，提出了一种声矢量阵声压与振速互协方差矩阵，论述了基于该互协方差矩阵的特征子空间分解原理。基于该互协方差矩
The acoustic vector sensor (AVS) is combined by traditional and omnidirectional pressure sensor and particle velocity sensor, which has natural dipole. It can measure both pressure and particle velocity of acoustic field at a point in space, whereas a traditional pressure sensor can only extract the pressure information. By taking advantage of the extra information, an acoustic vector sensor array (AVSA) is able to improve source localization performance without increasing array aperture size. In recent years, AVSA signal processing has been drawn much attention. And much work has been done for applying AVSA to the direction finding problem. However, these existing AVSA direction finding algorithms are all based on the same processing methodology that utilizes the particle velocity information of AVS as an independent array element (i.e. the particle velocity is regarded as an analog of the pressure information). Hence they don't make use of the pressure-velocity combined processing. In fact, there exists a coherence difference of pressure-velocity between radiated source signals based on the plane-wave model and the isotropic background noise. Therefore, the pressure-velocity combined processing has an outstanding ability to eliminate background noise in isotropic noise field.
At the same time, subspace-based DOA estimation algorithms such as MUSIC, ESPRIT, and so on, have attracted a lot of attention in recent years due to its high resolution and low computational complexity. Recently, researchers has introduced them to the signal processing for AVSA successfully. As stated above, however, existing methods do not utilize the pressure-velocity combined processing, so its threshold SNR (signal-to-noise ratio) is still relatively higher.
In this thesis we first review the fundamental principle of AVS, the physical basic of the pressure-velocity combined processing, as well as existing subspace methods based AVSA signal processing technologies. Its object is to investigate
与此同时，在DOA估计的诸多方法中，MUSIC、ESPRIT等基于特征分解理论的子空间类方法，以其较高的分辨能力和相对较小的计算复杂度而颇受关注，近来，已有学者将其引入到声矢量阵信号处理中，并取得了一定的效果。
本文在回顾声矢量传感器的基本原理、声压振速联合信息处理的物理基础以及基于子空间类方法的声矢量阵信号处理技术研究现状的基础上，着眼于将声压振速联合信息处理技术引入到声矢量传感器阵列信号处理中。通过将联合信息处理技术与子空间类高分辨测向技术相结合，为解决声矢量阵在低信噪比条件下的信源检测、目标定位和噪声测量等方面的应用问题，提供一种新的思路和方法。本文的主要创新点如下：
1) 在对声矢量传感器的基本原理以及声压振速联合信息处理的物理基础进行了深入分析的基础上，提出了一种声矢量阵声压与振速互协方差矩阵，论述了基于该互协方差矩阵的特征子空间分解原理。基于该互协方差矩
The acoustic vector sensor (AVS) is combined by traditional and omnidirectional pressure sensor and particle velocity sensor, which has natural dipole. It can measure both pressure and particle velocity of acoustic field at a point in space, whereas a traditional pressure sensor can only extract the pressure information. By taking advantage of the extra information, an acoustic vector sensor array (AVSA) is able to improve source localization performance without increasing array aperture size. In recent years, AVSA signal processing has been drawn much attention. And much work has been done for applying AVSA to the direction finding problem. However, these existing AVSA direction finding algorithms are all based on the same processing methodology that utilizes the particle velocity information of AVS as an independent array element (i.e. the particle velocity is regarded as an analog of the pressure information). Hence they don't make use of the pressure-velocity combined processing. In fact, there exists a coherence difference of pressure-velocity between radiated source signals based on the plane-wave model and the isotropic background noise. Therefore, the pressure-velocity combined processing has an outstanding ability to eliminate background noise in isotropic noise field.
At the same time, subspace-based DOA estimation algorithms such as MUSIC, ESPRIT, and so on, have attracted a lot of attention in recent years due to its high resolution and low computational complexity. Recently, researchers has introduced them to the signal processing for AVSA successfully. As stated above, however, existing methods do not utilize the pressure-velocity combined processing, so its threshold SNR (signal-to-noise ratio) is still relatively higher.
In this thesis we first review the fundamental principle of AVS, the physical basic of the pressure-velocity combined processing, as well as existing subspace methods based AVSA signal processing technologies. Its object is to investigate
引文
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