Unfolded Coprime Linear Array with Three Subarrays for Non-Gaussian Signals: Configuration Design and DOA Estimation.

Meng Yang,Changbo Ye,Jianfeng Li,Jingming Li

doi:10.3390/s22041339

Abstract

In this paper, we investigate the problem of sparse array design for the direction of the arrival (DOA) of non-Gaussian signals and exploit the unfolded coprime linear array with three subarrays (UCLATS) to obtain physical sensors location. With the motivation from the large consecutive degree of freedom (DOF), we optimize the process of obtaining physical sensors location from two steps. Specifically, the first is to model the process of obtaining the longest consecutive virtual sum co-array from a given number of physical array elements into a global postage-stamp problem (GPSP), whose solution can be employed to determine the locations of the longest possible consecutive sum co-array (2-SC) and initial physical array. The second step is to multiply the location of the virtual sum co-array by appropriate coprime coefficients to generate UCLATS and then multiply the initial physical array position by the same corresponding coefficients to obtain physical sensors location. Besides, an algorithm is proposed to obtain DOA estimates, which employs the discrete Fourier transform (DFT) method and partial spectrum searching multiple signal classification (PSS-MUSIC) algorithm to obtain initial estimates and fine estimates, respectively, termed as the DFT-MUSIC method. Compared with the traditional total spectrum searching MUSIC (TSS-MUSIC) algorithm, the DFT-MUSIC method performs the same asymptotical performance of DOA estimation with less than 10% complex multiplication times, which can be verified by numerical simulations under the same condition.

Highlights

Array signal processing (ASP) exploits the sensor array to receive spatial signals in order to obtain discrete observation data
direction of arrival (DOA) estimation plays an important role in array signal processing, which is widely applied in wireless communication system, radar system and navigation [1,6]
Conventional DOA estimation methods mainly focus on uniform linear array (ULA) [7,8] or uniform planar array (UPA) for their simple and symmetric structure, whose inter-element spacing is required to be no longer than half wavelength to avoid angle ambiguity, which results in limited DOA estimation accuracy

Summary

Introduction

Array signal processing (ASP) exploits the sensor array to receive spatial signals in order to obtain discrete observation data. To further improve the performance of DOA estimation, various sparse geometries are proposed to achieve an extended array aperture, enhanced degree of freedom (DOF) and reduced mutual coupling, whose typical representatives include coprime array (CA) [9,10,11,12], nested array (NA) [11], and minimum redundancy array (MRA) [13]. These sparse arrays are designed based on the SOC under the assumption that the sources are Gaussian, whose virtual elements can be obtained from the sum co-array (2-SC) or difference coarray (2-DC) of physical sensors. Scholars propose NA-based and CA-based arrays, such as augmented coprime array (ACA) [14], augmented nested array (ANA) [15], unfolded coprime linear array (UCLA) [16,17], coprime array with displaced subarrays (CADiS) [18], generalized nested array (GNA) [19], and so on

Methods

Results

Conclusion