Mesh-block-free sparse angle estimation method based on array element mutual coupling optimization

Abstract

The invention provides a meshless block sparse angle estimation method based on array element mutual coupling optimization, which is characterized by comprising the following steps of: 1, establishing an array receiving signal model under unknown array element mutual coupling interference; 2, constructing a block sparse representation model under unknown array element mutual coupling interference; step 3, establishing a sparse reconstruction model based on hybrid kernel-l1 norm minimization under block sparsity; 4, deducing an equivalent convex optimization reconstruction structure based on positive semidefinite; step 5, deriving a meshless Lagrange dual solution form; and step 6, solving by using a convex optimization algorithm to realize high-precision angle estimation. According to the method, the discrete grid error is effectively suppressed without calibrating the mutual coupling coefficient by using the non-grid block sparse reconstruction method, and the angle estimation precision under the unknown array element mutual coupling condition is improved.

Description

technical field [0001] The invention relates to the technical field of array signal processing, and specifically designs a gridless block sparse angle estimation method based on mutual coupling optimization of array elements. Background technique [0002] Array signal processing uses multiple antenna elements to realize multi-point parallel sampling processing of the spatial signal field, and obtains additional degrees of freedom in space by virtue of the spatial characteristics of the array. Different from general signal processing methods, array signal processing can effectively use the degree of freedom in space for flexible beam steering, and obtain higher signal gain and stronger signal anti-interference ability. After years of development, array signal processing technology has been successfully applied in fields such as astronomical observation, radar, sonar, communication system and biomedicine. Target angle and direction estimation is one of the important tasks in ...

AI Technical Summary

Problems solved by technology

However, when the array system has unknown array element mutual coupling interference, the sparse representation dictionary built on the basis of the array flow pattern will not be able to effectively represent the angular distribution of the spatial target, so the angle estimation performance under sparse reconstruction will deteriorate

On the other hand, the discrete grid mismatch problem is one of the inherent problems of sparse reconstruction algorithms

The premise of achieving angle estimation under effective sparse reconstruction is that the real target angle is contained on the pre-divided spatial discrete grid points, and the densely divided discrete dictionary grid helps to improve the accuracy of sparse representation, but too dense grid Points will lead to excessive correlation between sparse dictionary elements, which is not conducive to accurate sparse reconstruction, but also leads to higher dimensionality of sparse reconstruction and increased complexity of algorithm solution

In addition, the observation space where the target angle is located is continuous, and no matter how dense the discrete grid dictionary is, it cannot ensure that the real angle falls on the pre-divided grid points, causing discrete grid mismatch problems and off-grid errors. leading to a decrease in the accuracy of angle estimation

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