Unified coprime array with multi-period subarrays for direction-of-arrival estimation

Wang, Weijiang; Ren, Shiwei; Chen, Zhiming

doi:10.1016/j.dsp.2017.11.015

Cited by 41 publications

(24 citation statements)

References 21 publications

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“…Besides, from Definition 5, we can determine their respective index sets as = {1,1,1,1,1,1,1,1,1,2,2,2,2,2,3,4,5} , ℕ = {2,3,4,5,6,7,8,9,10,6,7,8,9, 10,6,6,6}, = {1,1,1,1,1,2,3,4,5,6,7,8,9,6,7,8,9}, and ℕ = {2,3,4,5,10,10,10,10,10,1,1,1,1,2,2,2,2}. As shown in Figure 2c, we then obtain the equivalent received arrays of NNA-I and NNA-IV, which are identical and can be expressed as = {5, 15,25,35,45,55,65,75,76, ⋯ ,84} . Finally, according to Equation (12), we can easily obtain this fully continuous SDCA.…”

Section: Propertymentioning

confidence: 99%

“…According to Equations (15), (20), and (21), it is easy to observe that the minimum and maximum sensor positions in NNA-I and NNA-II are 0, and S 1,a (N 1 ), respectively. Thus, we know that the physical apertures of NNA-I and NNA-II are identical and equal to S 1,a (N 1 ).…”

Section: Appendix B Proof Of Propertymentioning

confidence: 99%

“…To improve the detection performance of CPA and NA, many modified versions of them are proposed as well. For example, generalized CPA [14] and CPA with multiperiod subarrays [15] are designed to fill in the holes of virtual arrays and thus increase the available continuous DOFs. By contrast, thinned CPA (TCPA) proposed in [16,17] removes the redundant physical sensors without reducing the DOFs of virtual array.…”

Section: Introductionmentioning

confidence: 99%

“…Although the translational NA denoted by (15) can generate a continuous SDCA, it cannot be used directly as the received array. The reason has been mentioned in Section 2 that only the equivalent received array S has a direct connection with SDCA.…”

Section: Introduction Of Modified Translational Nested Array (Mtna)mentioning

confidence: 99%

See 3 more Smart Citations

Design of Novel Nested Arrays Based on the Concept of Sum-Difference Coarray

Peng

Hou

et al. 2020

Electronics

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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.

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Section: Propertymentioning

confidence: 99%

Section: Appendix B Proof Of Propertymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introduction Of Modified Translational Nested Array (Mtna)mentioning

confidence: 99%

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Design of Novel Nested Arrays Based on the Concept of Sum-Difference Coarray

Peng

Hou

et al. 2020

Electronics

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“…P ositioning technology is one of the important contents of signal processing in recent years， which is widely applied in earthquake monitoring, smart city, industrial 4.0 and so on [1,2,3,4]. The current positioning technologies are mainly divided into two-step positioning methods and direct position determinations(DPD) methods.…”

Section: Introductionmentioning

confidence: 99%

Direct Position Determination of Coherently Distributed Sources based on Compressed Sensing with a Moving Nested Array

2019

KSII TIIS

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The existing direct position determinations(DPD) for coherently distributed(CD) sources are mostly applicable for uniform linear array(ULA), which result in a low degree of freedom(DOF), and it is difficult for them to realize the effective positioning in underdetermined condition. In this paper, a novel DPD algorithm for coherently distributed sources based on compressed sensing with a moving nested array is present. In this algorithm, the nested array is introduced to DPD firstly, and a positioning model of signal moving station based on nested array is constructed. Owing to the features of coherently distributed sources, the cost function of compressed sensing is established based on vectorization. For the sake of convenience, unconstrained transformation and convex transformation of cost functions are carried out. Finally, the position coordinates of the distribution source signals are obtained according to the theory of optimization. At the same time, the complexity is analyzed, and the simulation results show that, in comparison with two-step positioning algorithms and subspace-based algorithms, the proposed algorithm effectively solves the positioning problem in underdetermined condition with the same physical element number.

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Generalized Structured Sparse Arrays for Fixed and Moving Platforms

Qin,

Qin

2023

Sparse Arrays for Radar, Sonar, and Communications

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Unified coprime array with multi-period subarrays for direction-of-arrival estimation

Cited by 41 publications

References 21 publications

Design of Novel Nested Arrays Based on the Concept of Sum-Difference Coarray

Design of Novel Nested Arrays Based on the Concept of Sum-Difference Coarray

Direct Position Determination of Coherently Distributed Sources based on Compressed Sensing with a Moving Nested Array

Generalized Structured Sparse Arrays for Fixed and Moving Platforms

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