Dilated Arrays: A Family of Sparse Arrays With Increased Uniform Degrees of Freedom and Reduced Mutual Coupling on a Moving Platform

Recently, dilated nested arrays have been proposed on a moving platform to increase the uniform degrees of freedom (uDOF) by a factor of three by exploiting array motion. However, no literature addresses the issue whether the same dilation method still performs well for other array geometries such a...

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Vydané v:IEEE transactions on signal processing Ročník 69; s. 3367 - 3382
Hlavní autori: Li, Shuang, Zhang, Xiao-Ping
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Array signal processing
Arrays
coprime arrays
Degrees of freedom
degrees of freedom (DOF)
difference coarray
dilated arrays (DAs)
Dilation
Direction of arrival
direction-of-arrival (DOA) estimation
Estimation
Geometry
Linear arrays
moving platform
Mutual coupling
nested arrays
Redundancy
Robustness (mathematics)
Sensor arrays
Sensors
Sparse arrays
Two dimensional displays
two-dimensional (2-D) sparse array
ISSN:1053-587X, 1941-0476
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Shrnutí:Recently, dilated nested arrays have been proposed on a moving platform to increase the uniform degrees of freedom (uDOF) by a factor of three by exploiting array motion. However, no literature addresses the issue whether the same dilation method still performs well for other array geometries such as coprime arrays, augmented nested arrays and minimum redundancy arrays. Compared with nested arrays, these arrays either achieve higher uDOF or exhibit more robustness to mutual coupling among sensors. In this paper, we propose a novel sparse array geometry named dilated arrays (DAs) on a moving platform by applying the dilation method to other array geometries. First, by exploiting the relationship between the element positions in the difference coarrays of the original linear array and the synthetic array after motion, we prove that, for a DA on a moving platform, the maximum uDOF can be tripled compared to that of its original array regardless of the array geometry. Therefore, the number of sources that can be resolved for direction-of-arrival (DOA) estimation is increased threefold. Second, we prove that a DA reduces mutual coupling compared with its original array. As a result, the DA is more robust to mutual coupling than its original array. Third, we extend one-dimensional DAs to the two-dimensional (2-D) case, yielding a new 2-D sparse array geometry named two-parallel DAs. We show that by exploiting array motion, two-parallel DAs can increase the number of detectable sources threefold. Numerical simulations demonstrate the superior performance of the proposed array geometries.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2021.3083988