Enhanced Nested Array Configuration With Hole-Free Co-Array and Increasing Degrees of Freedom for DOA Estimation

Zhao, Pinjiao; Hu, Guobing; Qu, Zhiyu; Wang, Liwei

doi:10.1109/lcomm.2019.2947585

Cited by 43 publications

(17 citation statements)

References 13 publications

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“…(b) EGNA has the same DOF as Yang-NA, but with less MC, especially for the larger coprime factors. (c) Both EGNA and GNA [24] have less MC, but DOF of EGNA is higher than that of NA, ENA [18], Iizuka-NA [19], SNA, CPA, LoDiNA, and GNA.…”

Section: Introductionmentioning

confidence: 97%

“…To this end, several modified configurations have been derived. Zhao et al [18] and Iizuka et al [19] adjusted the interelement spacing of NA and obtained optimized nested configurations with higher DOF. Yang et al [20] established an improved nested array by introducing an additional antenna, which has higher DOF than NA, and its DCA is a virtual uniform linear array without holes.…”

Section: Introductionmentioning

confidence: 99%

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DOA Estimation of an Enhanced Generalized Nested Array with Increased Degrees of Freedom and Reduced Mutual Coupling

Zhang

Shi

et al. 2021

International Journal of Antennas and Propagation

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Aiming at low degrees of freedom (DOF) and high mutual coupling (MC) of the existing sparse arrays, an enhanced generalized nested array (EGNA) is proposed in this paper. Specifically, the proposed array adds a single antenna on the basis of generalized nested array (GNA), and the difference of coprime factors is employed as the spacing between the second subarray and the additional antenna. Then, the values of the coprime factors are analyzed in detail, which indicates that Yang-NA can be explained as a special case. Compared with the majority of the existing sparse arrays, EGNA not only has the closed-form expressions of the physical antenna locations, consecutive lags, and unique lags, but also significantly increases DOF and reduces MC. In view of the above advantages, EGNA can obtain superior performance in direction of arrival (DOA) estimation. Numerical simulation results verify the rationality and superiority of the proposed nested array.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

DOA Estimation of an Enhanced Generalized Nested Array with Increased Degrees of Freedom and Reduced Mutual Coupling

Zhang

Shi

et al. 2021

International Journal of Antennas and Propagation

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“…As for the CPA, the mutual coupling can be alleviated for the configuration is constructed by a pair of coprime ULAs, which can offer O(MN) DOF with O(M + N) sensors, but has holes in the virtual coarray. Recently, lots of modified versions have been further developed, such as super NA [12], enhanced NA [13], augmented NA [14], generalized CPA [15], and thinned CPA [16], to increase the consecutive DOF and reduce the mutual coupling. e sparse arrays mentioned above construct virtual co-array from the view of difference co-array (DCA) and realize the multitarget DOA estimation by exploiting vector MUSIC method or compressed sensing (CS) approach; nevertheless, the number of resolvable sources cannot exceed twice of the physical aperture.…”

Section: Introductionmentioning

confidence: 99%

A Sliding Window Data Compression Method for Spatial-Time DOA Estimation

Zhao

Wang

2021

International Journal of Antennas and Propagation

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This paper presents a sliding window data compression method for spatial-time direction-of-arrival (DOA) estimation using coprime array. The signal model is firstly formulated by jointly using the temporal and spatial information of the impinging sources. Then, a sliding window data compression processing is performed on the array output matrix to realize fast calculation of time average function, and the computational burden has been reduced accordingly. Based on the concept of sum and difference co-array (SDCA), the vectorized conjugate augmented MUSIC is adopted, with which more sources than twice of the physical sensors can be resolved. Additionally, the sparse array robustness to sensor failure has been evaluated by introducing the concept of essential sensors. The theoretical analysis and numerical simulations are provided to confirm the effectiveness performance of the proposed method.

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“…As a result, the cost, volume, weight of the system will be greatly increased, and the payload capability will not be satisfied. Although the high-order cumulant algorithm [31][32][33][34][35] and the nested array [36][37][38][39][40] are used to extend the array aperture, the estimable maximum number of sources are restricted by the number of array elements inevitably, and the computational complexity will be greatly increased as well. Secondly, these algorithms are unable to estimate the very close DOAs for the restriction of the number of array elements.…”

Section: Introductionmentioning

confidence: 99%

DOA estimation algorithm based on DFT and multiple regression

Mao

Zhang

2020

IET Communications

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In order to address the problem that the direction-of-arrival (DOA) estimation is restricted by the number of array elements, this study comes up with a novel DOA estimation algorithm based on discrete-Fourier-transformation (DFT) and multiple regression, which focuses on the direct data domain from each array element rather than the traditional covariance domain of the whole array. In this algorithm, the authors regard each channel of the array signal model as an independent form of multiple regression and achieve the DOA by estimating the regression coefficient of the reconstructed source signal from each array element one by one iteratively. In addition, the DOAs estimated from the multiple array elements are averaged to improve the accuracy by exploiting the asymptotic normality. This algorithm is proposed, modelled, and developed theoretically at first. Then the capabilities that the authors' algorithm can estimate not only the very close DOAs since they distinguish the irrelevant sources by the DFT on temporal frequency but also the DOAs when the number of sources is larger than that of array elements are fully demonstrated by simulations. Furthermore, no requirement for the known number of sources brings the algorithm broader application foreground.

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Enhanced Nested Array Configuration With Hole-Free Co-Array and Increasing Degrees of Freedom for DOA Estimation

Cited by 43 publications

References 13 publications

DOA Estimation of an Enhanced Generalized Nested Array with Increased Degrees of Freedom and Reduced Mutual Coupling

DOA Estimation of an Enhanced Generalized Nested Array with Increased Degrees of Freedom and Reduced Mutual Coupling

A Sliding Window Data Compression Method for Spatial-Time DOA Estimation

DOA estimation algorithm based on DFT and multiple regression

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