In this paper, a new method is applied to get the computation formula of partial coherence function. The main attention is paid to the computation formula of the partial coherence function with three and four signals....In this paper, a new method is applied to get the computation formula of partial coherence function. The main attention is paid to the computation formula of the partial coherence function with three and four signals. The advantages of the method discussed in the paper are clear in physical meaning and easy to compute at the end of the paper,the application of the method to the identification of an air compressor noise source is presented and the results are satisfactory.展开更多
近场环境下传统多重信号分类(Multiple Signal Classification,MUSIC)算法无法对相干声源进行三维位置估计,针对该问题论文提出了一种近场相干声源三维定位算法。首先建立了近场球面波信号接收模型,其次结合空间平滑算法和修正MUSIC算...近场环境下传统多重信号分类(Multiple Signal Classification,MUSIC)算法无法对相干声源进行三维位置估计,针对该问题论文提出了一种近场相干声源三维定位算法。首先建立了近场球面波信号接收模型,其次结合空间平滑算法和修正MUSIC算法将矩形阵列分割成数个子阵列,对其协方差矩阵和求复共轭并左右同乘单位矩阵,以此来解决相干信号协方差矩阵秩亏损的问题,最后使用三维MUSIC算法完成近场声源三维位置估计。仿真表明:论文算法可有效地对相干信号进行解相干处理并准确地完成近场相干声源的三维位置估计。展开更多
提出一种可用于相干声源识别的快速反卷积声源成像算法(Fast deconvolution approach for the mapping of coherent acoustic sources,FC-DAMAS)。该算法去除了反卷积声源成像算法(Deconvolution approach for the mapping of acoustic ...提出一种可用于相干声源识别的快速反卷积声源成像算法(Fast deconvolution approach for the mapping of coherent acoustic sources,FC-DAMAS)。该算法去除了反卷积声源成像算法(Deconvolution approach for the mapping of acoustic sources,DAMAS)中的互谱过程,直接求解声源复数源强分布,从而避免了互谱操作导致的待求未知数个数的剧增,因此不再需要采用非相干声源假设来减少待求未知数,使该算法能够同时适用于相干和非相干声源的识别;其次,该算法在反卷积求解过程中采用了与稀疏约束反卷积声源成像算法(Sparsity constrained DAMAS,SC-DAMAS)类似的L1范数稀疏约束反卷积方法,使算法在相干和非相干声源的识别过程中均具有很高的计算精度和空间分辨率;此外,该算法中增加了对测量声压的主成分分析去噪过程,弥补了取消互谱去噪过程造成的算法鲁棒性下降,使算法具有与SC-DAMAS算法类似的噪声鲁棒性。与现有可用于相干声源识别的反卷积声源成像算法(Deconvolution approach for the coherent sources,DAMAS-C)相比,提出的FC-DAMAS算法大大降低了待求解的矩阵方程规模,使其计算效率得到了显著提升。通过数值仿真和实验验证了FC-DAMAS算法的优越性,结果表明所提出的FC-DAMAS算法在应用范围、声源识别性能和实用性方面都更具优势,更适于在实际工程中应用。展开更多
文摘In this paper, a new method is applied to get the computation formula of partial coherence function. The main attention is paid to the computation formula of the partial coherence function with three and four signals. The advantages of the method discussed in the paper are clear in physical meaning and easy to compute at the end of the paper,the application of the method to the identification of an air compressor noise source is presented and the results are satisfactory.
文摘近场环境下传统多重信号分类(Multiple Signal Classification,MUSIC)算法无法对相干声源进行三维位置估计,针对该问题论文提出了一种近场相干声源三维定位算法。首先建立了近场球面波信号接收模型,其次结合空间平滑算法和修正MUSIC算法将矩形阵列分割成数个子阵列,对其协方差矩阵和求复共轭并左右同乘单位矩阵,以此来解决相干信号协方差矩阵秩亏损的问题,最后使用三维MUSIC算法完成近场声源三维位置估计。仿真表明:论文算法可有效地对相干信号进行解相干处理并准确地完成近场相干声源的三维位置估计。
文摘提出一种可用于相干声源识别的快速反卷积声源成像算法(Fast deconvolution approach for the mapping of coherent acoustic sources,FC-DAMAS)。该算法去除了反卷积声源成像算法(Deconvolution approach for the mapping of acoustic sources,DAMAS)中的互谱过程,直接求解声源复数源强分布,从而避免了互谱操作导致的待求未知数个数的剧增,因此不再需要采用非相干声源假设来减少待求未知数,使该算法能够同时适用于相干和非相干声源的识别;其次,该算法在反卷积求解过程中采用了与稀疏约束反卷积声源成像算法(Sparsity constrained DAMAS,SC-DAMAS)类似的L1范数稀疏约束反卷积方法,使算法在相干和非相干声源的识别过程中均具有很高的计算精度和空间分辨率;此外,该算法中增加了对测量声压的主成分分析去噪过程,弥补了取消互谱去噪过程造成的算法鲁棒性下降,使算法具有与SC-DAMAS算法类似的噪声鲁棒性。与现有可用于相干声源识别的反卷积声源成像算法(Deconvolution approach for the coherent sources,DAMAS-C)相比,提出的FC-DAMAS算法大大降低了待求解的矩阵方程规模,使其计算效率得到了显著提升。通过数值仿真和实验验证了FC-DAMAS算法的优越性,结果表明所提出的FC-DAMAS算法在应用范围、声源识别性能和实用性方面都更具优势,更适于在实际工程中应用。