| Array signal processing is one of the most important branches of signal processing, applied rapidly in the areas of communication, radar, sonar, earthquake, reconnoiter, chronometer and etc., traditional methods of which focused on the study in Gauss noise condition. In these years, there have been researching deeply especially abroad in the field whereas non-Gauss noise exists widely among the nature. The main task of my study is to provide a kind of robust methods for beamforming technology with FLOS-based RBF networks under alpha stable noise condition. Beamforming technology plays a main role of array signal processing that is used to process array signals with vectors so as to intensify the desired ones and restrain the interfacial. Alpha stable distribution, the most important model of non-Gauss distribution, is offered to describe such a character of thick tails. It is remarkable that alpha stable distribution includes the situation of Gauss when α=2. So alpha stable distribution is also called generalized Gauss distribution. Beamforming technology includes such methods as characteristic-based methods, high-order cumulant methods, subspace-based methods (MUSIC and ESPRIT) and etc. It works out worse results when second order statistics meets non-Gauss noise and high-order cumulant algorithm brings enormous computation and slower convergence speed. Therefore, people take fractional lower order statistics (FLOS) algorithm that represents better robustness in the processing of alpha stable distributed noise into account. In order to reduce the computation complexity, it makes beamforming process as a nonlinear mapping from input space to output space that approached by RBF network. Avoiding impulsive noise affection, here it takes advantage of FLOS preprocessing before RBF network. In this paper, it compares RBF network to other classical algorithms in details and the simulation represents that RBF network with FLOS preprocessing under the environment of impulsive noise can approach this nonlinear mapping nearly. |
