A Novel Sparse Array for Localization of Mixed Near-Field and Far-Field Sources

Yinsheng Wang,Bin Ba,Weijia Cui,Fengtong Mei,Yuxi Du,Diego Masotti

doi:10.1155/2021/3960361

Yinsheng Wang, Bin Ba + Show 4 more

Open Access

https://doi.org/10.1155/2021/3960361

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Abstract

As we all know, nested array can obtain a larger array aperture and more degrees of freedom using fewer sensors. In this study, we not only designed an enhanced symmetric nested array (ESNA), which achieved more consecutive lags and more unique lags compared with a generalized nested array but also developed a special cumulant matrix, in the case of a given number of sensors, which can automatically generate the largest consecutive lags of the array. First, the direction-of-arrivals (DOAs) of mixed sources are estimated using the special cumulant matrix. Then, we can estimate the range of the near-field source in the mixed source using a one-dimensional spectral search through estimated DOAs, and in the mixed sources, the near-field and far-field sources are classified by bringing in the range parameter. The largest consecutive lags and composition method of ESNA are also given, under a given number of sensors.Our algorithm has moderate computation complexity, which provides a higher resolution and improves the parameters’ estimation accuracy. Numerical simulation results demonstrate that the proposed array showed an outstanding performance under estimation accuracy and resolution ability for both DOA and range estimation compared with existing arrays of the same physical array sensors.

Highlights

Mixed source localization is an important problem in the field of array signal processing such as radar, sonar, and communications [1,2,3,4,5]. erefore, to solve this problem, many algorithms have been proposed, such as the ESPRIT algorithm [6], the MUSIC algorithm [7], and so on
Based on the classical second-order statistics algorithms, an oblique projection algorithm was proposed by He et al [17] and Zuo et al [18] to eliminate the subspace of FF sources from the covariance matrix and obtain the corresponding subspace of NF sources
The performance of the proposed array is evaluated by simulation results, which are compared with the other three array geometries, symmetric nested array (SNA) [28], symmetric double nested array (SDNA) [29], and improved symmetric nested array (ISNA) [21], and we compared with high-order statistics (HOS) MUSIC [20]

Summary

Introduction

Mixed source localization is an important problem in the field of array signal processing such as radar, sonar, and communications [1,2,3,4,5]. erefore, to solve this problem, many algorithms have been proposed, such as the ESPRIT algorithm [6], the MUSIC algorithm [7], and so on. When there are only FF sources, this method fails To solve this problem, a RARE-based localization algorithm was proposed by Hua et al [19]. The methods mentioned above are all based on a uniform symmetric linear array to estimate the DOA and ranges of the mixed sources. According to the characteristics of a sparse array, an enhanced symmetric nested array (ESNA) and a novel algorithm for localization of mixed sources are proposed in this paper. (3) We verified the superiority of the proposed algorithm based on the ESNA in terms of mixed-field accuracy, resolution capacity, and many more DOFs and analyzed the range of ambiguity problem.

Signal Model

Proposed Algorithm

Simulation Results

Conclusion