Super-resolution time delay estimation using exponential kernel correlation in impulsive noise and multipath environments

The time delay estimation (TDE) is an important and fundamental part in wireless communication signal processing, which has been widely used in localization, intelligent navigation, and radio monitoring. However, it remains a challenging task to support such TDE mechanisms with super-resolution in impulsive noise and multipath environments. Aiming at improving the TDE performance in both impulsive noise and multipath environments, in this paper, a novel super-resolution TDE method is proposed by using the defined exponential kernel correlation (EKC) function. In the proposed TDE method, EKC function is defined to suppress the non-Gaussian impulsive noise. Specifically, the theoretical analysis is also given to prove its boundedness and its effectiveness in non-Gaussian impulsive noise suppression. Further, a novel super-resolution TDE method is proposed by combining EKC function and multiple signal classification (MUSIC) method, which can significantly improve the estimation performance in terms of resolution and accuracy with both non-Gaussian impulsive noise and multipath environments. Finally, simulation experiments demonstrate that the proposed super-resolution TDE method maintains good performance even with a low characteristic exponent and low generalized signal-to-noise ratio (GSNR).