An Efficient Compressive Sensing Direction Finding Method Based on Special Non-Uniform Linear Array

Abstract

The invention belongs to the field of array signal processing, and in particular relates to an efficient compressed sensing direction finding method based on a special non-uniform linear array. This method uses the structure of the special non-uniform linear array to expand the array, and combines the infinite norm to perform compressed sensing direction finding on the target in the impact noise environment, so as to obtain its optimal angle estimation value. The applicable environment of this method includes impact noise, Gaussian noise and strong impact noise, and is suitable for various and harsh direction finding environments. In addition, the designed infinite norm compressive sensing direction finding method can perform high-precision measurement of targets in the impact noise environment. direction, and at the same time can ensure the robustness of direction finding, and the special non-uniform linear array designed by this method has a variety of antenna placement methods without affecting the direction finding performance, which is suitable for more stringent placement requirements. Finally, this method greatly improves the resolution and precision of the direction finding method of compressed sensing, and has a wider application range.

Description

technical field [0001] The invention belongs to the field of array signal processing, and in particular relates to an efficient compressed sensing direction finding method based on a special non-uniform linear array. Background technique [0002] Direction of Arrival (DOA) estimation plays a pivotal role in the field of array signal processing. The rapid development of this technology in the field of military and national economy has brought great changes to array signal processing. Compressed sensing theory can compress and sample the signal at the same time, thus transforming signal samples into information samples. The theory points out that for any sparse or compressible signal in any transform domain, the signal can be sampled with a sampling frequency lower than that of Nyquist, and then the original signal can be sparsely reconstructed by solving an optimization problem. There are only a small amount of interesting signals in the observation space of DOA estimation, ...

AI Technical Summary

Problems solved by technology

[0003] Compressive sensing DOA estimation has many advantages, but most of the research on this theory is carried out under the condition of uniform linear array (Uniform Linear Array, ULA), as Wang Ying analyzed in the article "Directionestimation using compressive sampling array processing" In the article "Compressed Sensing Direction of Arrival Estimation Based on Non-Uniform Linear Array", Diao Ming designed a method of compressive sensing direction-finding method for non-uniform linear array under Gaussian noise environment. , but there are many problems in this method, such as the ability of the non-uniform linear array to expand the aperture of the array is not strong, it lacks a strict theoretical basis, and it is only effective in a Gaussian noise environment

[0004] When performing direction finding in harsh noise environments such as impact noise, the robustness of direction finding is difficult to guarantee, and the difficulty of direction finding will also increase. Therefore, it is necessary to design high-performance Robust Compressed Sensing Direction Finding Method for

No matter in the environment of Gaussian noise or impact noise, the classical compressed sensing direction finding method is difficult to get rid of the shortcomings of poor convergence accuracy and direction finding failure when the number of antennas is too small, so it is necessary to design a new robust compressed sensing method based on a special non-uniform linear array Direction-finding method, through this method to solve the problem of compressed sensing direction-finding in the impact noise environment, and finally realize that it can also have better performance in harsh noise environments such as impact noise

Images