DOA estimation method in co-prime array based on iteration sparse reconstruction

Abstract

The invention discloses a DOA estimation method in a co-prime array based on iteration sparse reconstruction. A receiving antenna array uses a nonlinear co-prime array, through vectorized processing on a second-order statistical characteristic covariance matrix of a received signal, and a difference array in larger aperture length can be determined, so as to improve detection capability. Dispersing processing is performed on the angle domain where targets are in, targets can be regarded as sparsely distributed on grid points or near grid points, and sparse signal reconstruction problems on logarithm and forms are established. Using convex compact upper bounds of logarithm and a function, an original sparse problem is reestablished, to dynamically adjust and update discrete points of the angle domain in an iterative manner, so approach the actual arrival angle of the target.

Description

technical field [0001] The invention relates to the technical field of communication signal processing, in particular to a DOA estimation method based on iterative sparse reconstruction using a coprime array. Background technique [0002] Signal processing methods based on antenna arrays are widely used in many fields such as wireless communication, electromagnetic field, radar, and sonar. Direction of Arrival (DOA) estimation is an important problem in the field of array signal processing. [0003] The traditional estimation method usually researches the uniform linear array whose element interval is half wavelength, and is suitable for occasions where the number of detection targets is less than the number of array elements. For example, for N-antenna uniform linear arrays, traditional estimation methods (such as based on estimation method, etc.) can detect at most N-1 targets, and the nonlinear coprime array constructs the original array into a differential array with mo...

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Problems solved by technology

[0003] The traditional estimation method usually researches the uniform linear array whose element interval is half wavelength, and is suitable for occasions where the number of detection targets is less than the number of array elements. For example, for N-antenna uniform linear arrays, traditional estimation methods (such as based on estimation method, etc.) can detect at most N-1 targets, and the nonlinear coprime array constructs the original array into a differential array with more virtual antennas and a larger aperture length by using the characteristics of the covariance matrix, which can significantly improve Its degree of freedom, that is, the detection ability, the traditional estimation method usually requires prior information on the number of targets and a large sample to estimate the direction of arrival of the target, which is not applicable under the conditions of small samples and unknown number of targets. In addition, the traditional estimation method It is also difficult to apply to the situation of low signal-to-noise ratio, that is, weak targets with low transmission power (such as less than noise power) may not be detected

[0004] In recent years, with the development of sparse reconstruction theory, sparse representation has gradually begun to be applied in wavelet denoising, radar imaging and other fields. The DOA estimation method based on sparse signal reconstruction can fully exploit the advantages of high degrees of freedom of coprime arrays. Compared with traditional methods, the number of detection targets is significantly increased. However, conventional sparse reconstruction methods need to perform static rasterization on the angle domain, and the establishment of 0 Norm (usually l p Norm approximation, p≤1) is a sparse optimization problem that minimizes the target. The problem with this type of method is that the estimation accuracy is heavily dependent on the initial rasterization process. If most of the targets are located at or very close to the grid points, the estimation performance Good; on the contrary, if most of the objects drift outside the grid points, the estimation performance is difficult to guarantee. Therefore, the grid mismatch problem caused by the static grid processing of the angle domain will seriously affect the reconstruction effect

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