Abstract
Recently, the difference and sum (diff-sum) coarray has attracted much attention in one-dimensional direction-of-arrival estimation for its high degrees-of-freedom (DOFs). In this paper, we utilize both the spatial information and the temporal information to construct the diff-sum coarray for planar sparse arrays. The diff-sum coarray contains both the difference coarray and the sum coarray, which provides much higher DOFs than the difference coarray alone. We take a planar coprime array consisting of two uniform square subarrays as the array model. To fully use the aperture-extending ability of the diff-sum coarray, we propose two novel configurations to improve the planar coprime array. The first configuration compresses the inter-element spacing of one subarray and results in a larger consecutive area in the coarray. The second configuration rearranges the two subarrays and introduces a proper separation between them, which can significantly reduce the redundancy of the diff-sum coarray and increase the DOFs. Besides, we derive the closed-form expressions of the central consecutive ranges in the coarrays of the proposed array configurations. Simulations verify the superiority of the proposed array configurations.
Highlights
Two-dimensional (2D) direction-of-arrival (DOA) estimation has attracted much attention recently for its wide applications in the fields of radar, sonar, communications and navigation, etc
In this sub-section, we provide the comparison of uniform DOFs (uDOFs) of our proposed array configurations and several other 2D array configurations
This result comes from the fact that coprime array with two separated subarrays (CATSS) combines the consecutive part of all difference and sum coarrays, and that 2D nested array combines its positive and negative difference coarrays with only one overlapped line
Summary
Two-dimensional (2D) direction-of-arrival (DOA) estimation has attracted much attention recently for its wide applications in the fields of radar, sonar, communications and navigation, etc. Conventional research mainly focuses on the uniform rectangular arrays (URAs) [1,2], whose inter-element spacing between the adjacent sensors is no more than the typical half-wavelength λ/2. The URAs are easy to construct, the detection ability is limited by the number of sensors. The increasing sensor number brings higher hardware cost and computational complexity. It is significant to design arrays with sparse geometries and the ability to detect more sources than physical sensors. In the past five years, numbers of novel one-dimensional (1D) sparse arrays with the concept of a coarray have been proposed to achieve a higher number of degrees-of-freedom (DOFs) than
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