Research On The Algorithm Of Compressed Sensing DOA Estimation Via Sparse Bayesian Inference

Direction of Arrival(DOA)can be widely used in many fields.Therefore,various methods for estimating the direction of the arrays have received much attention.However,in the case of the existing direction-of-arrival estimation algorithms,when some parameter environments deteriorate rapidly(such as low signal-to-noise ratio environment,or too small number of snapshots),the results are not satisfactory.In a typical experimental environment,the number of signals is often limited,so the spatial spectrum is often sparse.Therefore,by reasonably utilizing the compressed sensing technology and utilizing the sparse attributes inherent in the array signal model,the original signal can be recovered completely and without distortion through some sparse reconstruction algorithms,even in the case of low SNR and low snapshot,the related algorithms still have good results.In this paper,the related DOA estimation algorithm is studied by the sparse Bayesian algorithm under compressed sensing.First of all,this paper introduces the background of the subject,briefly summarizes the research history and current status of the direction of arrival estimation,and introduces two algorithms for array signal processing and direction finding.At the same time,the related background and development of compressed sensing are introduced,and the theoretical basis of the estimation of the direction of arrival is applied.The signal model of the direction of arrival estimation under the compressed sensing framework is introduced,and the feasibility of related sparse reconstruction is discussed.The algorithm focuses on the superiority of sparse Bayesian as a sparse reconstruction algorithm.Next,this paper starts with the sparse Bayesian compressed perceptual direction finding algorithm under the grid,and uses the improved covariance matrix to establish a covariance sparse Bayesian layered model.The maximum a posteriori estimation is used to reconstruct the sparse signal,and the network is realized.The direction of the direction of the wave is estimated.Through computer simulation experiments,the effectiveness of the algorithm is true and performance of the algorithm is useful.Finally,this thesis studies the grid mismatch,that is,the case where the direction of the incoming signal does not correspond to the grid one by one,and establishes a block sparse Bayesian model that can be better applied to the direction-of-arrival estimation model,using the Taylor expansion formula.Improve the accuracy of the grid error and achieve high-precision direction of arrival estimation.Through computer simulation experiments,the effectiveness of the algorithm is verified and the performance of the algorithm is analyzed accordingly.