Distributed Super Nested Arrays: Reduce the Mutual Coupling between Array Antennas

Hongyong Wang,Xiaochuan Wu,Ying Suo,Weibo Deng,Yanmo Hu,Xin Zhang,Giovanni Andrea Casula

doi:10.1155/2021/1563164

Abstract

In array, mutual coupling between the antennas is inevitable, which has an adverse effect on the estimation of parameters. To reduce the mutual coupling between the antennas of distributed nested arrays, this paper proposes a new array called the distributed super nested arrays, which have the good characteristics of the distributed nested arrays and can reduce the mutual coupling between the antennas. Then, an improved multiscale estimating signal parameter via rotational invariance techniques (ESPRIT) algorithm is presented for the distributed super nested arrays to improve the accuracy of direction-of-arrival (DOA) estimation. Next, we analyze the limitations of the spatial smoothing algorithm used by the distributed super nested arrays when there are multiple-source signals and the influence of the baseline length of distributed super nested arrays on the accuracy of DOA estimation. The simulation results show that the distributed super nested arrays can effectively reduce the mutual coupling between the array antennas, improve the DOA estimation performance, and significantly increase the number of detectable source signals.

Highlights

Direction-of-arrival (DOA) estimation is a major application of the antenna array [1], whose accuracy is related to the aperture of the array and the mutual coupling between the array antennas. e aperture of the array is an important factor affecting the accuracy of DOA estimation. erefore, it is necessary to increase the aperture of the array to improve the accuracy of DOA estimation. e distributed arrays are usually composed of multiple subarrays with a large baseline length that can effectively increase the aperture of the array and significantly improve the accuracy of parameter estimation
The maximum number of detectable source signals that can be resolved with an N antenna uniform linear arrays (ULA) using traditional subspace-based methods like multiple signal classification (MUSIC) [2] is N-1
To effectively solve this problem, sparse arrays [3,4,5,6], such as nested arrays [7,8,9,10], minimum redundancy arrays (MRAs) [11], and coprime arrays [12, 13], are proposed. ese sparse arrays are capable of providing a dramatic increase in the degrees of freedom (DOF) and can resolve more source signals than the actual number of physical antennas

Summary

Introduction

Direction-of-arrival (DOA) estimation is a major application of the antenna array [1], whose accuracy is related to the aperture of the array and the mutual coupling between the array antennas. e aperture of the array is an important factor affecting the accuracy of DOA estimation. erefore, it is necessary to increase the aperture of the array to improve the accuracy of DOA estimation. e distributed arrays are usually composed of multiple subarrays with a large baseline length that can effectively increase the aperture of the array and significantly improve the accuracy of parameter estimation. It can be seen from the simulation results that in the case of multiple source signals, considering the mutual coupling between the array antennas, the estimation accuracy of the distributed super nested arrays is better than that of the distributed nested arrays, super nested arrays, nested arrays, distributed uniform arrays, and ULA.

Results

Conclusion