Improved Fast-ICA Algorithm In The Application Of LPI Radar Detection And Recognition

Blind source separation is an important research direction of signal processing. After years of studying by the domestic and foreign scholars, for now a variety of different algorithms are proposed for blind source separation with different signal forms, and its theoretical system is also increasingly perfect. However, with the development of modern separation technology and the increasingly complex electromagnetic environment, traditional blind source separation algorithms have been unable to solve the problem of blind source separation in noise environment. In this paper, the application of improved Fast-ICA algorithm in multi component detection and recognition in the background of noise is studied.First, the blind source separation in traditional noise free environment is studied. Traditional algorithm such as Infomax algorithm, Extended-ICA algorithm and Fast-ICA algorithm were studied mainly. These algorithms can separate the mixed signal in noise free environment, but the signal recovery result is not ideal.Secondly, the blind source separation is studied under the background of noise. The traditional Fast-ICA algorithm and EASI algorithm are analyzed at first,and the Fast-ICA algorithm and EASI algorithm are integrated, and an improved Fast-ICA algorithm is proposed and applied in noise environment. The proposed algorithm has fast convergence and can separate the mixed signal in noise environment.Finally, the detection and recognition of LPI radar emitter signal is studied. The difficulty of the detection and recognition of multi - target component is that the cross terms are generated when the signals are mixed. The improved Fast-ICA algorithm can separate the mixed signals,so that the cross terms can be eliminated single component. The PWVD of signal is used to detect and recognize the signal to get the desired signal parameters. And through the analysis of the improved LPI radar signal detection and recognition algorithm, it is shown that the improved algorithm is accurate in parameter estimation.