Detection of Known Signals Under Non-Gaussian Noise Based on the Laplacian Kernel

This paper presents a new robust detector employing the Laplacian kernel function to detect known signals in environments with impulsive noise. The effectiveness of the detector is assessed by comparing it to several widely used detectors. Monte Carlo simulations were also conducted to determine the optimal kernel width for specific noise conditions and to evaluate its asymptotic relative efficiency compared to the local optimum detector. The detector's excellent performance and its ability to function without prior knowledge of the noise enables it to be an alternative to current detection methods.