Generalized cross correlation time delay estimation based on improved wavelet threshold function

Usually the acoustic has non-stationary and low signal to noise ratio characteristics, which will reduce the accuracy of time delay estimation. In order to resolute this problem and improve the accuracy of time delay estimation. In this paper a novel wavelet denoising algorithm and Generalized Cross Correlation (GCC) algorithm are combined to improve the accuracy of the traditional time delay estimation methods. First, this paper studies the wavelet threshold denoising principle and the structure of the traditional threshold functions, puts forward the improved wavelet threshold function. Second, according to the function curve of the denoising performance analysis, the theoretical analysis shows that it improves the problem of constant bias in soft threshold function and hard threshold function inconsistent problem. The experimental results show that the output signal to noise ratio of the signal is improved by using the improved wavelet threshold function, which can effectively restrain the noise of the signal. Then the improved wavelet threshold function and the GCC are combined to propose a GCC time delay estimation method based on the improved wavelet threshold function. Simulation results show the proposed method in this paper can effectively suppress noise and reduce the fluctuation of generalized cross correlation function and make the peak more sharp, so that time delay estimation is more accurate.