Abstract
Nested arrays have recently attracted considerable attention in the field of direction of arrival (DOA) estimation owing to the hole-free property of their virtual arrays. However, such virtual arrays are confined to difference coarrays as only spatial information of the received signals is exploited. By exploiting the spatial and temporal information jointly, four kinds of novel nested arrays based on the sum-difference coarray (SDCA) concept are proposed. To increase the degrees of freedom (DOFs) of SDCA, a modified translational nested array (MTNA) is introduced first. Then, by analyzing the relationship among sensors in MTNA, we give the specific positions of redundant sensors and remove them later. Finally, we derive the closed-form expressions for the proposed arrays as well as their SDCAs. Meanwhile, different index sets corresponding to the proposed arrays are also designed for their use in obtaining the desirable SDCAs. Moreover, the properties regarding DOFs of SDCAs and physical apertures for the proposed arrays are analyzed, which prove that both the DOFs and physical apertures are improved. Simulation results are provided to verify the superiority of the proposed arrays.
Highlights
Direction of arrival (DOA) estimation of multiple signals is a hot topic in the area of array signal processing since it can be widely used in radar, sonar, and remote diagnosis, etc. [1,2,3,4,5]
By using the elements of sum coarray (SCA) to fill in the holes of difference coarray (DCA), the VCAM method can achieve the increase of continuous DOFs and improve the detection performance of coprime array (CPA)
From Reference [25], we find that there exist holes in the sum-difference coarray (SDCA) of INAwSDCA-I, which means it cannot be completely utilized for DOA estimation
Summary
Direction of arrival (DOA) estimation of multiple signals is a hot topic in the area of array signal processing since it can be widely used in radar, sonar, and remote diagnosis, etc. [1,2,3,4,5]. The generalized nested subarrays reported in [20,21] have advantages in increasing the DOFs and physical apertures, which can be termed as robust NAs. Note that, the virtual arrays of CPA and its modifications are discontinuous, which indicates that they cannot be totally used for DOA estimation when the subspace-based methods are employed [10]. In comparison with other sparse arrays, the proposed structures possess larger physical apertures and can generate the same SDCA with significantly increased continuous DOFs. By introducing the translational NA [25] and modifying it, we first propose the modified translational NA (MTNA). By jointly exploiting the spatial and temporal information of received data, SDCA of a physical array is constructed, which can contribute to the increase of continuous DOFs. its generation process has been discussed detailedly in this paper.
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