基于支持向量回归的patch近场声全息研究
Patch near-field acoustic holography based on support vector regression
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摘要: 为了解决patch近场声全息中全息数据外推问题,提出一种基于支持向量回归的一步式patch近场声全息技术。该方法首先把初始全息面上的数据当成训练样本进行学习,构造出回归函数,然后利用回归函数实现全息数据外推,最后基于统计最优近场声全息进行重建。数值仿真和实验研究的结果表明:在各个分析频率下,该方法都可以实现小孔径全息面的近场外推。从近场声全息重建结果看,即使初始全息数据受到噪声干扰,该方法也是一种有效的patch近场声全息技术。Abstract: A one-step patch near-field acoustic holography technique based on support vector regression is proposed to solve hologram data extrapolation problem.The regression functions are constructed by treating the measured data on the patch hologram as training samples,and the data outside the measured aperture are extrapolated.Finally,the sound field is reconstructed by means of statistically optimized near field acoustic holography.Both numerical and experimental results show that the extrapolation of the measured sound pressure outside the patch hologram aperture is achieved easily.Furthermore,the reconstruction results show that the proposed method is an effective patch near-field acoustic holography technique even the initial hologram data is disturbed by noise.