Research On DOA Estimation Techniques Based On Nonuniform Arrays

Direction-of-arrival(DOA)estimation is one of the hotspots in the field of signal processing.This technology is not only widely used,but also plays a more and more important role in communication,radar and many other areas.Over the past few decades,a series of efficient DOA estimation algorithms have been proposed.In recent years,while the theory of compress sensing(CS)theory gets mature and wildly used,the DOA estimation algorithm based on sparse reconstruction is more and more concerned and studied.In DOA estimation using uniform linear arrays(ULA),the degree of freedom(DOF)is directly limited by the number of elements.The use of spatial smoothing to deal with the correlated signals will further lead to smaller array aperture,reduced resolution and loss of DOF.Since the non-uniform linear array(NLA)can obtain larger array aperture and higher degree of freedom,and the placement of array elements also has greater flexibility,so the research on NLA design and corresponding DOA estimation algorithms has significance importance in practical applications.The goal of this thesis is to estimate the DOAs of narrow-band far-field signals using NLA.The contents of this thesis mainly focus on designing the NLA to improve the DOF of the array and investigating the DOA estimation algorithms according to the characteristics of the NLA.The methods including the coprime array,the nested array and the partly-filled non-uniform linear arrays(PFNLA)are studied and simulated.For the DOA estimation of the correlated or coherent signals,two estimation methods based on sparse reconstruction technology are proposed.The main contributions and innovative points in this thesis are listed as follows:1.For the issue of DOA estimation of correlated or coherent signals using NLA,a DOA estimation method based on sparse reconstruction technology is proposed.This method mainly uses the sparse reconstruction technology to make an initial estimate and determine the interpolation area.Compared with existing algorithms,this new method has higher estimation accuracy.2.In order to achieve the goal of increasing the degree of freedom of the array and estimating the correlated or coherent signals,a low dimensional iterative sparse reconstruction algorithm based on covariance is proposed.Compared with the traditional subspace method,this method can not only obtain larger array aperture and higher degree of freedom,but also has lower computational complexity than the existing sparse covariance algorithm.