Time Delay Estimation of Generalized Cross Correlation with Hassab-Boucher Weighted Function Based on Singular Value Decomposition

Traditional time delay estimation technology of generalized cross correlation based on direct data, without the impacts of various environment noises and fluctuation of outlier, results the greatly attenuated of its accuracy. A novel time delay estimation (TDE) method is proposed to solve the problem. This method is called generalized cross correlation method with Hassab-Boucher (HB) based on singular value decomposition (SVDHB-GCC). Firstly, adopting the singular value decomposition (SVD) is not only to reduce the effect of environmental noise, but improve the SNR of received signals. Secondly, the peak of GCC function can be sharped by introducing the Hassab-Boucher weighted function into the calculation of the power spectral density. Finally, the post processing approach based on mean and median is exploited, which can obtain the optimal estimation of TDE by revising its abnormal fluctuation with the unknown initial value of time delay. Simulation results show that the proposed method has lower percentage of abnormal points (PAP) and root mean square error (RMSE), and the reverse is true in the accurate ratio (AR) of TDE, compared with other methods in low signal-noise ratio (SNR).