Electromagnetic Signal Separation Based On Underdetermined Blind Source Separation Algorithm

The rapid development of information technology promotes the wide application of various electronic information systems and also leads to the increasingly complex electromagnetic environment.However,the complex electromagnetic environment makes the electromagnetic signal easily superimposed with other interference signals in the transmission process,which affects the normal work of the information system.As a popular signal processing technology,blind source separation(BSS)can separate each source signal according to the observed mixed-signal,and then extract the useful signal to remove the influence of the interference signal.The problem that the number of observed signals is less than the number of source signals is a difficult point in underdetermined blind source separation(UBSS)research,but it has a great research significance because it is more in line with the actual situation.In this paper,the UBSS algorithm of typical electromagnetic signals is researched.In this paper,the theory and method of underdetermined blind source separation are firstly introduced,and then the two-step method based on sparse component analysis is used to divide UBSS into two processes: mixing matrix estimation and source signal separation.In the mixing matrix estimation,an improved algorithm is proposed to overcome the deficiency of the traditional K-means clustering algorithm.The algorithm eliminates noise interference through outlier detection,and uses affinity propagation clustering to estimate the number of source signals and the initial clustering center,and finally uses the K-means algorithm to estimate the mixing matrix.The simulation experiment results show that the algorithm can effectively improve the accuracy of the mixing matrix estimation and have a wider scope of application.In the source signal separation stage,the compressed sensing theory is introduced to build the compressed sensing model of the underdetermined blind source separation system,and the piecewise K-SVD dictionary learning algorithm is used to construct a redundant dictionary to seek the sparse representation of the source signal.Aiming at the shortcomings of the multiple iterations of the orthogonal matching pursuit(OMP)algorithm and low data utilization,the generalized orthogonal matching pursuit algorithm based on the Dice coefficient is used to reconstruct the source signal.The simulation results show that the reconstruction algorithm can effectively improve the separation accuracy.Finally,an electromagnetic interference-assisted diagnosis system is designed based on the proposed algorithm,and the radiation interference emission experiment of the crystal oscillator circuit is carried out in the anechoic chamber.Using the proposed algorithm and the designed system,the radiated interference signals are separated with a good effect.