Nonlinear Adaptive Narrowband-Interference Mitigation in Generalized Gaussian Noise Channels

Abstract

A nonlinear adaptive interference mitigation (AIM) algorithm is introduced when the signal of interest is a broadband signal, the additive strong interference is a narrowband signal, and its channel noise distribution belongs to generalized Gaussian distributions. The nonlinear function for the new AIM algorithm is obtained by using Taylor series ezpansion and properties of the generalized Gaussian distributions. Its filter weights are adoptively adjusted through the normalized LMS algorithm. Through MonteCarlo runs, its performance is demonstrated and compared with that of ezisting linear and nonlinear AIM algorithms, when the channel noise distribution is Laplace.