The Research On DOA Estimation And Its Application

DOA estimation is widely applied in sonar array direction finding,mobile communication and other fields,and it is one of the crucial problems of signal parameter estimation in array signal processing.The estimation of the number of sources is very crucial,which affects the performance of DOA estimation.The known number of sources is an essential prerequisite for many DOA estimation algorithms.When the estimated number of sources is not equal to the actual number of sources,the performance of DOA estimation algorithms deteriorates sharply or even lose efficacy.Therefore,accurate estimation of the number of sources will affect the application of DOA estimation algorithm in various fields.When the noise received by the array does not meet the white noise condition,the traditional high-resolution information theory method will lose efficacy completely,in which case,the classical Gerschgorin disk estimation(GDE)algorithm can be used to estimate the number of sources.Although the classical GDE algorithm can accurately estimate the number of sources in the background of spatial colored noise,the traditional GDE algorithm needs to manually select the adjustment factor and the number of sources should not exceed M-2.Therefore,this thesis proposes an improved new Gerschgorin disks estimation(NGDE)algorithm to solve these problems of GDE algorithm.Accurate estimation of the number of sources can improve the performance of DOA estimation methods.High performance DOA estimation can be used to develop angle dimension spectrum to promote the effective utilization of spectrum.In this thesis,the high-resolution Multiple Signal Classification(MUSIC)algorithm is applied to cognitive radio to realize spatial angle dimension spectrum sensing,and the spatial angle dimension sensing technology of non-circular signal is studied to improve the utilization of spectrum resources.The research contents of this thesis include:1.Different array models and DOA estimation methods are compared.MUSIC algorithm is mainly studied.The influence of source number estimation results on MUSIC algorithm is analyzed theoretically and verified by simulation experiments.2.An improved algorithm for source number estimation(NGDE)based on Gerschgorin disk criterion is proposed to address the problems of the traditional GDE algorithm.The proposed algorithm uses a fixed judgment criterion and matrix upgrading to improve the shortcomings of the classical GDE algorithm,and improves the estimation performance of the algorithm by matrix elementary transformation and iterative averaging.Simulation experiments verify that the proposed algorithm not only has better estimation performance in the white noise background,but also outperforms the classical GDE algorithm and the information theoretic method based on the diagonal loading technique.3.By applying MUSIC to the localization phase of the "detection-location" spatial angle-dimensional spectrum sensing scheme,and considering the non-circular characteristics of the signal,an spatial angle-dimensional spectrum sensing method called NC-?-MTG-MUSIC is proposed.This method first uses the original array output and its conjugate to form the extended virtual array output,and then simplifies the covariance matrix of the extended virtual array output by using the signal non-circular characteristic,detects the primary user using a fusion algorithm(?-MTG)incorporating Maximum Eigenvalue-Trace(MET)algorithms and Maximum Eigenvalue to the Geometric Mean(MEGM)algorithms for the covariance matrix of the extended virtual array output.When the presence of the primary user is detected,the MUSIC algorithm is used to estimate the azimuth and elevation angle of the primary user signal on the covariance matrix of the extended virtual array output,so as to realize the spatial angle-dimensional spectrum sensing and enabling the cognitive user to access the spectrum even in the presence of the primary user,thus achieving the purpose of increasing the spectrum access opportunities.The simulation experimental results verify the effectiveness and feasibility of the algorithm.