ABSTRACT

In this paper, we first propose a novel sparse array (NSA) composed of three adjoined sparse subarrays, among which the first and third ones can form the prototype coprime array (CA), and the second one is nested to them. When deploying NSA for direction of arrival (DOA) estimation, two virtual CAs with larger apertures than the physical ones can be generated by vectorising the covariance matrices corresponding to specific subarray pairs. Accordingly, improved DOA estimation performance can be achieved. Afterwards, to enhance the DOA estimation of noncircular sources, four improved NSAs are designed, which have minimised redundancy between the difference and sum coarrays. Hence, they have larger utilised apertures than NSA. In addition, due to the larger physical aperture and inter-element spacing, all the five proposed arrays have less mutual coupling and better DOA estimation performance than the prototype CA. Numerical simulations confirm their effectiveness and superiority.

摘要

在本文中，我们首先提出了一种新的稀疏数组（NSA），由三个相邻的稀疏子数组组成，其中第一个和第三个可以形成原型互质数组（CA），第二个嵌套在它们之间。当部署 NSA 进行到达方向 (DOA) 估计时，可以通过向量化对应于特定子阵列对的协方差矩阵来生成两个孔径比物理 CA 更大的虚拟 CA。因此，可以实现改进的DOA估计性能。之后，为了增强非圆形源的 DOA 估计，设计了四个改进的 NSA，它们最大限度地减少了差和和协阵列之间的冗余。因此，它们具有比 NSA 更大的利用孔径。此外，由于较大的物理孔径和元件间距，所有五个提议的阵列都比原型 CA 具有更少的相互耦合和更好的 DOA 估计性能。数值模拟证实了它们的有效性和优越性。