Sum and difference coarrays based 2-D DOA estimation with co-prime parallel arrays

Shi, Junpeng; Hu, Guoping; Zhang, Xiaofei; Gong, Pan

doi:10.1109/wcsp.2017.8171037

Cited by 4 publications

(9 citation statements)

References 21 publications

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“…The performances of two kinds of TPNAs and 2-D DOA estimation method are compared with PCPA [ 27 ], S-CPPA [ 28 ] and TPCPA [ 29 ], as well as the respective 2-D DOA estimation method. What needs to be noticed is that in [ 27 ] and [ 29 ], the normalized elevation angles are obtained by a least squares technique, while the total least squares technique is used in [ 28 ] and our method, and an augmented matrix was constructed in [ 29 ] to obtain two snapshot virtual data vectors to enhance the estimation performance. The parameter of inter-element unit spacing was chosen to be equal to

for all array configurations.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Due to the finite snapshot sampling, there are errors existing in the augmented virtual received data vector

and its corresponding estimated manifold matrix

. The total least squares technique [ 28 , 35 ] which has been demonstrated efficient in the errors-in-variables linear regression model, can be used in Equation (37) to estimate the equivalent virtual source signal vector

. Accordingly, we can obtain the solution of the equivalent virtual source signal vector

…”

Section: The Proposed Three-parallel Nested Subarrays and 2-d Doa mentioning

confidence: 99%

“…However, only the positive part of the difference coarray was considered in [ 26 ], which does not fully exploit the advantages of coprime property. So as to achieve higher DOFs, some modified versions, including parallel coprime subarrays (PCPA) [ 27 ], symmetric coprime parallel arrays (S-CPPA) [ 28 ] and three-parallel co-prime arrays (TPCPA) [ 29 ], have been successively proposed. They can dramatically increase the continuous DOFs with systematical design and analysis.…”

Section: Introductionmentioning

confidence: 99%

“…With the use of vectorization, one augmented virtual array with hole-free property is achieved, and the 2-D DOA estimation problem is transformed into two separate 1-D DOA estimation problems naturally. Finally, we propose to use the sparse representation framework [ 26 , 27 , 28 , 30 ] and total least squares technique [ 28 , 31 ] to estimate the paired 2-D angle parameters accurately and automatically. In this way, up to

sources can be resolved effectively with

sensors, where

.…”

Section: Introductionmentioning

confidence: 99%

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Two-Dimensional DOA Estimation for Three-Parallel Nested Subarrays via Sparse Representation

Peng

Hou

et al. 2018

Sensors

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Nested arrays are considered attractive due to their hole-free performance, and have the ability to resolve normalO(N2) sources with normalO(N) physical sensors. Inspired by nested arrays, two kinds of three-parallel nested subarrays (TPNAs), which are composed of three parallel sparse linear subarrays with different inter-element spacings, are proposed for two-dimensional (2-D) direction-of-arrival (DOA) estimation in this paper. We construct two cross-correlation matrices and combine them as one augmented matrix in the first step. Then, by vectorizing the augmented matrix, a hole-free difference coarray with larger degrees of freedom (DOFs) is achieved. Meanwhile, sparse representation and the total least squares technique are presented to transform the problem of 2-D DOA searching into 1-D searching. Accordingly, we can obtain the paired 2-D angles automatically and improve the 2-D DOA estimation performance. In addition, we derive closed form expressions of sensor positions, as well as number of sensors for different subarrays of two kinds of TPNA to maximize the DOFs. In the end, the simulation results verify the superiority of the proposed TPNAs and 2-D DOA estimation method.

show abstract

for all array configurations.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Due to the finite snapshot sampling, there are errors existing in the augmented virtual received data vector

and its corresponding estimated manifold matrix

. Accordingly, we can obtain the solution of the equivalent virtual source signal vector

…”

Section: The Proposed Three-parallel Nested Subarrays and 2-d Doa mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

sources can be resolved effectively with

sensors, where

.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Two-Dimensional DOA Estimation for Three-Parallel Nested Subarrays via Sparse Representation

Peng

Hou

et al. 2018

Sensors

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show abstract

“…The set of sensor elements is denoted by S in this work. Note that other authors using this set might call it L for example [31,32], as noted in Table 1.…”

Section: B Terminology Of 2d Arraysmentioning

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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2-D DOA Estimation Algorithm for Three-Parallel Co-prime Arrays via Spatial–Temporal Processing

Yu,

Liu,

Chen

et al. 2024

Circuits Syst Signal Process

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Sum and difference coarrays based 2-D DOA estimation with co-prime parallel arrays

Cited by 4 publications

References 21 publications

Two-Dimensional DOA Estimation for Three-Parallel Nested Subarrays via Sparse Representation

Two-Dimensional DOA Estimation for Three-Parallel Nested Subarrays via Sparse Representation

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

2-D DOA Estimation Algorithm for Three-Parallel Co-prime Arrays via Spatial–Temporal Processing

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