函数波束形成声源识别性能分析及应用

发布时间：2018-01-21 22:30

本文关键词： 声源识别波束形成互谱特征值分解试验　出处：《机械工程学报》2017年04期 　论文类型：期刊论文

【摘要】：函数波束形成声源识别算法能够有效克服传统波束形成存在的"主瓣"宽、"旁瓣"高且多等缺陷。基于传统波束形成及矩阵特征值分解理论,给出函数波束形成算法的理论推导。基于自主开发的算法程序进行单声源、不相干双声源的模拟仿真,结果表明:函数波束形成在识别单声源、不相干声源时均能有效地衰减旁瓣,提高识别分辨率,且能清晰地识别弱源;当声源不在聚焦网格点时,函数波束形成主瓣峰值输出随指数的增大而降低,出现失真的情况;传声器及测量通道频响失配情况对函数波束形成的成像影响很小。由函数波束形成峰值输出误差随指数的变化曲线,建议指数一般取16。进一步,不相干四声源的算例试验验证了算法的有效性及程序的正确性。对某汽车前围板的隔声薄弱环节识别中,除空调进风口及暖风机进出水管安装孔隔声薄弱环节以外,亦准确定位转向柱安装孔附近这一隔声薄弱位置,识别结果更全面,为进一步改进其隔声性能指明了方向。
[Abstract]:The function beamforming acoustic source recognition algorithm can effectively overcome the defects of "main lobe" width and "sidelobe" high and multiple in traditional beamforming based on traditional beamforming and matrix eigenvalue decomposition theory. The theoretical derivation of the function beamforming algorithm is given. Based on the self-developed algorithm program, the simulation of single sound source and incoherent dual sound source is carried out. The results show that the function beamforming is in the identification of single sound source. The non-coherent sound source can attenuate the sidelobe effectively, improve the recognition resolution, and clearly identify the weak source. When the sound source is not focused on the grid point, the peak output of the main lobe of the function beamforming decreases with the increase of the exponent, resulting in distortion. The influence of microphone and measurement channel frequency mismatch on the imaging of function beamforming is very small. From the curve of peak output error of function beamforming with exponent, it is suggested that the exponent should be taken as 16.6. The validity of the algorithm and the correctness of the program are verified by an example of non-coherent four-source sound source. In addition to the air conditioning inlet and the heater fan inlet and outlet pipe installation hole sound insulation weak link, but also accurately locate the steering column installation hole near this sound insulation weak position, the recognition results are more comprehensive. It points out the direction for further improving its sound insulation performance.
【作者单位】：重庆大学机械传动国家重点实验室;重庆大学汽车工程学院;重庆工业职业技术学院车辆工程学院;
【基金】：国家自然科学基金(51275538) 重庆市重大应用开发计划(cstc2015 yykfc60003)资助项目
【分类号】：TN64;TN912.3
【正文快照】： 0前言*近年来,波束形成噪声源识别方法已成为航空、列车、汽车、发动机等领域不可缺少的声源识别技术[1-17]。延时求和[5-7]、互谱成像函数[13-16]等传统波束形成(Conventional beamforming,CB)算法虽然具有计算速度快、测量距离远、易于布置等优点,但其“主瓣”过宽造成空间

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