Sparse Off-grid DOA Estimation Method With Array Error Calibration

In the field of array signal processing,DOA(Direction of Arrival)estimation is an important research hotspot,which is widely used in radar,communication system,sonar and biomedical imaging.In the actual array system,there are a wide range of error errors,such as mutual coupling effect of array elements,array amplitude and phase inconsistency and array element position inaccuracy,which will lead to a certain degree of deviation of array flow pattern,resulting in significant negative impact on the performance of most existing DOA estimation algorithms.Based on the relevant theories of compressed sensing and spatial spectrum estimation,this paper USES the sparse and sparse Bayesian learning methods of DOA in the spatial domain to realize the joint estimation of wave arrival direction and array error parameters.In the traditional system model based on compressed sensing(CS),the spatial domain is discretized into grids,and the quantization error of discrete grids limits the further improvement of estimation performance.In this paper,DOA estimation under three common array model errors is studied.The main research contents are summarized as follows:Firstly,DOA estimation based on sparse Bayesian learning method is studied and analyzed,indicating its superiority over traditional DOA estimation.Secondly,it is concluded from theoretical analysis that the DOA estimation method based on sparse Bayesian learning method has two shortcomings: bias of noise variance estimation and quantization error introduced by spatial discrete supercomplete model.Aiming at the inaccuracy of noise variance estimation,the unbiased noise variance estimation suitable for sparse Bayesian learning is obtained with the help of information theory and related papers.Aiming at the quantization error problem introduced by spatial discrete supercomplete model,the de-meshing lattice sparse model is used to jointly estimate off-Grid parameters and DOAs on the sampling grid to realize high-precision estimation of the arrival direction.Then,for the mutual coupling effect between array direction finding system of yuan,improved Bayesian learning method based on the above,a new observation model,this paper puts forward a kind of sparse array mutual coupling error off-grid DOA estimation algorithm,iterative updated including DOAs,array error vector and the parameters of the noise power and off-grid vector of unknown parameters.Based on sparse de-meshing Bayesian learning method,the joint estimation of wave arrival direction and array amplitude and phase error and the joint estimation of wave arrival direction and array position error are realized.Finally,the simulation results show that compared with the existing methods,the proposed algorithm significantly improves the DOA estimation performance in the presence of array error.