Signal Analysis, Digital Beamforming

Digital beamforming is the research focus of mobile communication system nowadays. This dissertation makes a study of the signal analysis in digital beamforming.With respect to the actual adaptive beamforming receiver system, the received array signal processing method is analyzed, then the principle of optimum weight value is deduced. After that, the processing of beamforming is pointed out. Aiming at the cascaded receiver system, we analyze the performance parameters and simulate the course.Then we conclude the errors in beamforming. Origin of the errors are deduced respectively and the model of error are established, and relation between them and performance of beamforming are presented.The cyclostationary of signal is introduced. AM and FM signals are demonstrated, then we study the influence of cyclostationary functions of noise to performance of beamforming. CAB algorithm and its operation is deduced, and its limitation is studied.At last, we propose some algorithms based on CAB algorithm.Then in terms of small snapshots, the performance of CAB algorithm is depressed. A method based on sub-array combined with correlation function is introduced to CAB algorithm. At a cost of aperture, the number of snapshots is equivalent to reduplication.With introducing the theory of eigen-space, ESB method in CAB algorithm is studied. The principle of the new method is analyzed.The influence caused by noise sub-space is analyzed and a new method is presented, which can realize reducing noise affect. Through the rough estimation of DOA, weight value is projected in signal plus interference sub-space in order to improve the performance.Forget factor is introduced to CAB algorithm. With analysis on cyclostationary frequency error, a method with forget factor is proposed to maintain the performance in the case of CFfi.Because of the great calculation of inverse correlation matrix in CAB algorithm, real-time control is difficult. We adopt multi-stages technique in CAB algorithm, the dimension of correlation matrix can decrease enormously.