声学测温中时间延迟估计的仿真研究

Simulation of Time Delay Estimation of Acoustic Pyrometry

准确的声波飞渡时间在声学测温中具有非常重要的意义,针对强噪声环境下弱信号检测问题,研究了声信号的时间延迟估计。指出了声学测温中几种传统的时延估计方法的不足,提出了一种基于小波神经网络的时延估计方法。通过设定不同的信噪比,分别对基于ML互相关方法和基于小波神经网络方法进行了时延估计仿真试验。结果表明,在较低的信噪比下,小波神经网络方法仍可以抑制非高斯信号中相关高斯噪声的影响,为声学测温中的声波飞渡时间测量提供了指导。

The accurate transmitted time of sound wave in acoustic pyrometry has the vital significance,the time delay estimation （TDE） of weak sound signal under the condition of strong noise is studied. The disadvantages of some traditional methods of TDE are shown. A new TDE method based on Wavelet Neural Networks is given. The simulation tests of TDE are done based on the ML cross-correlation method and Wavelet Neural Networks method respectively under different signal noise ratio （SNR）. The result shows that the influence of correlated Gaussian noise can be restrained from the non-Gaussian signal based on the Wavelet Neural Net- works method even under poor SNR. It is useful to the measurement of transmitted time of sound wave in acoustic pyrometry.