Direction Of Arrival Estimation Algorithms Based On Sparse Representation

As an important research direction in array signal processing,DOA estimation has been widely used in many fields such as radar detection,wireless communication,seismic exploration and aeronautical navigation.It has achieved rapid development especially in the past decade.With the increase of DOA estimation technology application scenarios,the signal environment in practical applications has become increasingly sophisticated,which will inevitably put forward higher requirements for the adaptability of DOA estimation technology under different scenarios and the performance of the method.At the same time,the traditional DOA estimation methods are not satisfactory in the scenes of small snapshots,low SNR or coherent signals,which are far from meeting the needs of practical applications.Therefore,how to use a small amount of measurement data to achieve high-resolution,robust DOA estimation has become a hot issue to be solved.At the same time,the rise of sparse representation theory and compressed sensing theory has attracted the attention of many scholars,which provides a new research perspective and theoretical support for the direction-of-arrival estimation problem.Different from the conventional subspace-based methods,the sparsity-based DOA estimation methods use the fact that the airspace of incoming signal is attribute with sparse structure,the traditional DOA estimation problem can be converted to a problem of sparse reconstruction.This kind of method not only demonstrates excellent adaptability to the aforementioned imperfect scenarios,but also achieves high-precision and high-resolution estimation effects.Therefore,the DOA estimation based on sparse representation theory has received extensive attention in recent years.This paper focuses on the sparse representation theory and analyzes the DOA estimation problem in depth,and proposes two new methods.The main work of this thesis is listed as follows:1)Briefly stated the related basic knowledge of the DOA estimation and compressive sensing theory.The conditions for the accurate recovery of sparse signals are expounded,and then we analyzed the rationality of the sparse reconstruction in the DOA estimation.2)This paper discussed several classical DOA estimation algorithms based on sparse representation in detail.3)Aiming at the advantages and disadvantages of existing DOA estimation algorithms based on sparse representation,two improved robust algorithms are proposed from the perspective of sparse representation of the vectorization of the covariance matrix of array data.One is an enhanced sparse representation algorithm based on the vectorization of the covariance matrix for Gaussian white noise.The other is an iterative estimation algorithm based on sparse representation under non-uniform noise.Finally,using a large number of simulation experiments to verify the effectiveness of the proposed algorithms.