Research On Blind Communication Signals Extraction Technology Based On Improved Independent Component Analysis With Reference Algorithm

Regarding to the sensitive wireless transmission system, the signal intensity reduces by a large margin and the signal incorporates interference signals during transmission. In addition, with the increase of digital communication services, the limitation of frequency spectrum becomes more and more evident. As a result, single channel blind source separation(SCBSS) has found many applications in communication area, such as electronic reconnaissance and radio monitoring. Due to the lack of separability of the received signals and the limitation of application range of algorithms, SCBSS problem is still a puzzling problem in the field of communication. Therefore, it is important to research on SCBSS problem.As a typical underdetermined BSS, SCBSS is a ill-posed problem, which is showed by analysis based on traditional separation criteria and algorithm, thus we must thoroughly analyze the properties of source signals, and make full use of their prior information to design an optimizational criterion. This thesis researches on blind source separation(BSS) for single channel communication signals. Firstly, this thesis explains the theories of independent component analysis(ICA), constrained independent component analysis(CICA), and independent component analysis with reference(ICA-R), which can solve BSS problem. Regarding to the deficiency of those algorithms, an improved ICA-R algorithm is proposed referring to a novel objective function, which is derived by adding the reciprocal of similarity measure to the standard contrast function, then the Lagrange multiplier method is adopted on the novel objective function, and the optimal weighted vector is obtained efficiently. As a result, the interested source signals can be extracted. Simulation results show that the proposed algorithm can recover the source signal. In addition, average running time of the algorithm is less than the tradition algorithms and the similarity between the recovered signal and source signal is higher by quantitative analysis and comparison.Furthermore, based on prior information of the received communication signal for wireless transmission system, the proposed ICA-R algorithm is utilized to convert aunderdetermined blind source separation problem into a well-posed problem. As a result, the desired communication source signal can be extracted by a special linear transformation. Simulation results show that the proposed ICA-R algorithm is able to recover the desired source signals with a high extracted accuracy and yield good performance.