Symmetry-Imposed Rectangular Coprime and Nested Arrays for Direction of Arrival Estimation With Multiple Signal Classification

Adhikari, Kaushallya; Drozdenko, Benjamin

doi:10.1109/access.2019.2948503

Cited by 26 publications

(9 citation statements)

References 40 publications

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“…This includes computing the deviation (error) from the known DoA; where the root mean square error (RMSE) is the most widely used metric [31-38, 40-58, 60-63]. However, other metrics can be used like the mean square angular error (MSAE) [88], mean square error (MSE) [89], and maximum root mean square error (MRMSE) [90]. In addition, when the 2D-DoA algorithm is expected to work in the underdetermined case, i.e.…”

Section: Performance Metricsmentioning

confidence: 99%

A Review of Sparse Sensor Arrays for Two-Dimensional Direction-of-Arrival Estimation

et al. 2021

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arrays are fundamental to localization applications. Specifically spares arrays can provide superior direction-of-arrival (DoA) estimation performance with limited number of sensors. There has been increased interest in the research community in designing 2D sparse arrays with performance improvement and complexity reduction. The research efforts are uncoordinated resulting in some repetitions and sometimes conflicting claims. After introducing 2D sparse arrays and their importance, this paper establishes the 2D-DoA estimation model and consolidates the performance metrics. An extensive literature overview of sparse arrays for 2D-DoA estimation is presented with an attempt to categorize existing works. The examined arrays include parallel arrays, L-shaped, V-shaped, hourglass, thermos, nested planer, and coprime planner, to name a few. Existing designs are compared in terms of required number of sensors, degrees of freedom (DOF), algorithm used, associated complexity and aperture size. The focus is on describing the sparse arrays, yet some specific details on DoA estimation algorithms are provided for selected array geometries. Fundamental problems of 2D-DoA estimation are outlined and existing solutions to alleviate these problems are discussed. This should be useful in predicting the estimation performance and required complexity; thus, aiding the decision of selecting a sensor geometry for DoA estimation. This review serves as a starting point for researchers interested in exploring or designing new 2D sparse arrays. It also helps to identify the gaps in the field and avoids unnecessary minor design modifications.

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Section: Performance Metricsmentioning

confidence: 99%

A Review of Sparse Sensor Arrays for Two-Dimensional Direction-of-Arrival Estimation

et al. 2021

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“…This matrix is denoted by T DAM in the sequel. The DAM can be directly used with eigenanalysis based DOA estimation algorithms such as MUSIC [20], [21]. However, the DAM is not guaranteed to be positive semi-definite [17], [18].…”

Section: Problem Formulationmentioning

confidence: 99%

Absolute Eigenvalues-Based Covariance Matrix Estimation for a Sparse Array

Adhikari¹

2021

Preprint

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The ensemble covariance matrix of a wide sense stationary signal spatially sampled by a full linear array is positive semi-definite and Toeplitz. However, the direct augmented covariance matrix of an augmentable sparse array is Toeplitz but not positive semi-definite, resulting in negative eigenvalues that pose inherent challenges in its applications, including model order estimation and source localization. The positive eigenvalues-based covariance matrix for augmentable sparse arrays is robust but the matrix is unobtainable when all noise eigenvalues of the direct augmented matrix are negative, which is a possible case. To address this problem, we propose a robust covariance matrix for augmentable sparse arrays that leverages both positive and negative noise eigenvalues. The proposed covariance matrix estimate can be used in conjunction with subspace based algorithms and adaptive beamformers to yield accurate signal direction estimates.

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“…Compared with other existing 2D sparse array configurations [10]- [12], CPAs are more attractive because of their limited mutual coupling effect property. To offer a better understanding of CPAs and facilitate the future research in this field, in this letter, CPAs are investigated from the perspective of difference coarrays.…”

Section: Introductionmentioning

confidence: 99%

Hole Locations and a Filling Method for Coprime Planar Arrays for DOA Estimation

2021

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Coprime linear arrays (CLAs) have drawn lots of attention. Efforts have been made to reveal the hole locations in the difference coarrays of CLAs, based on which many methods have been proposed to fill the holes, lengthening the consecutive difference coarray part and increasing the effective degrees of freedom (DOFs). Compared with CLAs, two dimensional (2D) coprime planar arrays (CPAs) are more relevant to real applications. However, no closed-form expressions for the hole locations in the difference coarray of a CPA have been found in the open literature, and the advantage in terms of DOFs of CPAs has not been well explored. In this letter, the structure of the difference coarrays of CPAs is investigated and the exact expressions of all hole positions are provided. Then, a holesfilling method is proposed, by which the most critical holes in the difference coarray can be filled such that a difference coarray with more consecutive lags as well as higher effective DOFs can be obtained.

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Symmetry-Imposed Rectangular Coprime and Nested Arrays for Direction of Arrival Estimation With Multiple Signal Classification

Cited by 26 publications

References 40 publications

A Review of Sparse Sensor Arrays for Two-Dimensional Direction-of-Arrival Estimation

A Review of Sparse Sensor Arrays for Two-Dimensional Direction-of-Arrival Estimation

Absolute Eigenvalues-Based Covariance Matrix Estimation for a Sparse Array

Hole Locations and a Filling Method for Coprime Planar Arrays for DOA Estimation

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