传声器阵列函数反卷积声源成像算法研究

反卷积声源成像算法具有较高的空间分辨率，但动态范围不理想导致成像结果中存在虚假声源。针对此问题，提出函数反卷积声源成像算法，利用函数波束形成算法改进成像性能，通过对反卷积声源成像算法中的点扩散函数进行幂次运算，建立函数波束形成输出功率、升幂点扩散函数与声源分布之间的线性关系，形成函数反卷积声源成像算法方程组；进一步采用正约束的高斯-赛德尔迭代方法，求解声源分布信息。对单声源和非相干声源仿真与实验表明，与函数波束形成、反卷积声源成像算法相比，所提出算法可有效改善成像分辨率及动态范围。根据声学中心覆盖面积随幂指数的变化关系，建议算法中幂指数取值范围为6～14。

Functional deconvolutional approach for the mapping of acoustic sources algorithm of microphone array

The DAMAS algorithm has high spatial resolution. But, the unsatisfactory dynamic range leads to false acoustic sources in the imaging results. The F-DAMAS algorithm is proposed to solve this problem, which exploits the functional beamforming ( FB) algorithm to improve the imaging performance. By powering the PSF in the DAMAS algorithm, the linear relationship among the output power of the FB algorithm, the powered PSF and the acoustic source distribution is established. And the equation group of the F-DAMAS is formed. Furthermore, the Gauss-Seidel iterative method with positive constraints is utilized to derive the acoustic source distribution. Compared with FB and DAMAS algorithms, simulations and experiments on mono and incoherent acoustic sources show that the proposed algorithm can effectively improve resolution and dynamic performance of the image. According to the relationship between the coverage area of the acoustic center and the change of the power index, it recommends that the value range of the power index in the algorithm is 6 ~ 14.