Abstract: The step parameters directly affect the performance of the kurtosis maximization blind beamforming algorithm,especially when the information of channels and sources is unknown,so it is difficult to choose step parameters.Two fast fixed point blind beamforming algorithms without any step parameters are proposed for arbitrary non-Gaussian signals.First of all,the dataset is preprocessed by whitening,then the cost function is constructed by using kurtosis maximization and orthogonal weights.Through optimizing the cost function by complex Newton-Like method,the new blind beamforming algorithms are introduced.Compared with the kurtosis maximization blind beamforming algorithm,this proposed algorithms have less error,fast convergence rate and no need of any step parameters,which are more suitable for a unknown channel and source environment.Simulation proves the efficiency of the algorithms.