基于压缩感知(CS)理论的等效源法(ESM)已逐步应用于近场声全息(NAH)领域以减少空间采样点数量并扩大声源识别的频率范围。针对空间连续型声源,本文提出了一种压缩奇异值分解等效源法(CSVDESM)来提高声场重建与声源识别性能。该方法首先利用等效源法对要重建的声场进行建模,然后使用奇异值分解法获取声场的一系列正交基,在CS框架下对声场进行重构。最后结合高阶矩阵函数波束形成理论对CSVDESM的输出结果进行修正,通过提高阶次值,不断缩小识别到的声学中心覆盖范围,提高声源识别定位精度。数值仿真分析和实验应用均验证了该方法的有效性与实用性。

Equivalent Source Method (ESM) based on compressed sensing (CS) theory is being applied to Nearfield acoustic holography (NAH) gradually to reduce the spatial sampling points and broaden the frequency range of sound source identification. For the spatially extended sound source, a compressed singular value decomposition equivalent source method (CSVDESM) is proposed to improve the performance of sound field reconstruction and the sound source identification. The sound field to be reconstructed is first modeled using ESM. Then a series of orthogonal basis of the source field are obtained by the singular value decomposition, and the reconstruction is accomplished in the CS framework. Finally, combined with the high-order matrix function beamforming, the output of CSVDESM is modified and the identified acoustic center coverage is continuously narrowed by increasing order value, and hence the accuracy of sound source identification can be improved. Numerical simulation and experiment verify the validity and practicality of CSVDESM.

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国家自然科学基金项目（面上项目，重点项目，重大项目）项目号：11874096