基于二维稀疏阵列的相控阵雷达超分辨角度估计方法研究——二维稀疏阵列结构分析

Research on Super-Resolution Angle Estimation Method of Phased Array Radar Based on Two-Dimensional Sparse Array—Two-Dimensional Sparse Array Structure Analysis

随着时代不断发展,基于均匀阵列的传统DOA估计算法已经落后于时代,稀疏阵列作为新型阵列能提高阵列自由度、降低阵元互耦效应。其中二维稀疏线阵相比于一维稀疏线阵性能更好、使用更广泛。本文总结了前人关于二维稀疏阵列的研究成果,主要研究内容如下:介绍了稀疏阵列。稀疏阵列的阵元间距不必再是信号半波长,降低了阵元间互耦效应,并通过构造虚拟阵列提高阵列自由度,扩展了阵列孔径。本文首先介绍了稀疏阵列是如何构造虚拟阵列并如何扩展阵列孔径这一核心理论,接着介绍了最小冗余阵列、嵌套阵列和互质阵列三种基础的阵列模型,并拓展介绍了L型互质阵列和精简型互质阵列模型,以及基于它们的DOA估计。

With the continuous development of the times, the traditional DOA estimation algorithm based on uniform array has lagged behind the times, and the sparse array, as a new type of array, can improve the degree of freedom of the array and reduce the cross-coupling effect of array elements. Among them, the two-dimensional sparse line array has better performance and is more widely used than the one-dimensional sparse line array. In this paper, we summarize the previous research results on two-dimensional sparse arrays, and the main research contents are as follows: sparse arrays are introduced. The spacing of the array elements of the sparse array no longer needs to be the half-wavelength of the signal, which reduces the mutual coupling effect between the array elements, and improves the array degree of freedom and expands the array aperture by constructing a virtual array. In this paper, we first introduce the core theory of how to construct virtual arrays and expand the aperture of sparse arrays, and then introduce the three basic array models of least redundant arrays, nested arrays and coprime arrays, and expand the L-shaped coprime array and compact coprime array models, as well as the DOA estimation based on them.