Beamspace U-ESPRIT DOA Estimation Algorithm of Coherently Distributed Sources in Massive MIMO Systems

Liu, Yang; Hou, Lina; Shen, Q; Lv, Cheng; Na, Shun; Qiu, Tianshuang

doi:10.1109/icaci49185.2020.9177726

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…estimating signal parameters via rotational invariance technique (ESPRIT) and its extended algorithms [9][10][11] also perform well. To summarize, this kind of research focuses on how the received signal is processed, and is based on the traditional ULA [12].…”

Section: Typical Cla Structurementioning

confidence: 99%

“…Furthermore, weighted MUSIC (w-MUSIC) is introduced in [6], which reveals the relationship between MUSIC and other DOA estimation algorithms such as minimum variance method (MVM) [7] and maximum entropy method (MEM) [8]. Other algorithms, such as estimating signal parameters via rotational invariance technique (ESPRIT) and its extended algorithms [9][10][11] also perform well. To summarize, this kind of research focuses on how the received signal is processed, and is based on the traditional ULA [12].…”

Section: Related Workmentioning

confidence: 99%

“…By far, much research has been conducted to solve the DOA estimation problem [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], among which the modified multiple signal classification (MUSIC) algorithm based on co-prime linear array (CLA) [10][11][12][13][14][15][16][17][18][19] is considered a high-performance technique. A CLA is composed of two uniform linear arrays (ULAs) as subarrays, and provides a much larger degree of freedom (DOF) than ULAs with the same number of physical sensors, which means that CLA is capable of sensing the incident signals more accurately.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

DOA Estimation Algorithm for Reconfigurable Intelligent Surface Co-Prime Linear Array Based on Multiple Signal Classification Approach

et al. 2022

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Co-prime linear arrays (CLAs) provide an additional degree of freedom (DOF) with a limited number of physical sensors, and thus help to improve the resolution of direction of arrival (DOA) estimation algorithms. However, the DOF of traditional CLA is restrained by the structure of the array, which cannot be adjusted after deployment. In this paper, we propose a DOA estimation algorithm for reconfigurable intelligent surface co-prime linear array (RIS CLA) based on the multiple signal classification approach. Specifically, an RIS CLA is first constructed on the ground of RIS antenna, by turning on/off specific elements at different times. Then, the covariance matrix of the received signal is vectorized, so as to construct a virtual difference array, whose aperture is considerably expanded. Finally, a spectral peak search on the noise subspace of the received signal of the difference array is conducted to obtain the DOA estimation result. Simulations verify the improvement of the proposed algorithm in terms of DOF and resolution. To be specific, the DOF provided by RIS CLA outnumbers that of CLA by more than 30%, and the resolution of the proposed DOA estimation algorithm is effectively improved, with its accuracy increased up to 70% under the low signal-noise-ratio (SNR) scenario, compared with existing algorithms.

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Section: Typical Cla Structurementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DOA Estimation Algorithm for Reconfigurable Intelligent Surface Co-Prime Linear Array Based on Multiple Signal Classification Approach

et al. 2022

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“…The direction of arrival (DOA) estimate is widely utilized in radar array systems, smart antenna, adaptive beamforming, and other applications as a key technology of passive radar [1][2][3][4][5][6][7][8][9]. In recent years, with the rapid development of massive multiple-input multiple-output (MIMO) technology, the DOA estimation method for massive antenna array has become a focus of research [10][11][12][13][14][15][16][17][18]. However, massive antenna array confronts several challenges, including a large number of antenna components, high computational complexity, and high power consumption, among others.…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity One-Bit DOA Estimation for Massive ULA with a Single Snapshot

Fan

Wang

et al. 2022

Remote Sensing

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Existing one-bit direction of arrival (DOA) estimate methods based on sparse recovery or subspace have issues when used for massive uniform linear arrays (MULAs), such as high computing cost, estimation accuracy depending on grid size, or high snapshot-number requirements. This paper considers the low-complexity one-bit DOA estimation problems for MULA with a single snapshot. Theoretical study and simulation results demonstrate that discrete Fourier transform (DFT) can be applied to MULA for reliable initial DOA estimation even when the received data are quantized by one-bit methods. A precise estimate is then obtained by searching within a tiny area. The resulting method is called one-bit DFT. This method is straightforward and simple to implement. High-precision DOA estimates of MULA can be obtained with a single snapshot, and the computational complexity is significantly less than that of existing one-bit DOA estimation methods. Moreover, the suggested method is easily extensible to multiple snapshot scenarios, and increasing the number of snapshots can further improve estimation precision. Simulation results show the effectiveness of the one-bit DFT method.

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“…In [22], the BDML algorithm for elevation estimation in MIMO radar is proposed to reduce the computational burden. In [23], the BD U-ESPRIT algorithm performs 2D estimation of azimuth angle and elevation angle for coherently sources in MIMO radar. The BD U-ESPRIT algorithm choose proper and few beams in 2D-BD to reduce computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Sum and Difference Beamspace Domain Refined Maximum Likelihood Algorithm for Elevation Estimation in Multipath Environment

Chen

Zhao

et al. 2021

Preprint

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Beamspace domain super-resolution methods for elevation estimation in multipath environment has attracted significant attention. However, the difference beam is rarely used in super-resolution methods, especially in low elevation estimation. In order to improve the accuracy of beamspace domain maximum likelihood algorithm for elevation estimation, we apply the difference beam to the beamformer. Therefore, we develop a sum and difference beamspace domain maximum likelihood (SDBDML) algorithm for elevation estimation, which has good estimation accuracy and is not sensitive to antenna height and reflection coefficient errors. In order to further improve the estimation performance, we take the refined signal model into account in the SDBDML algorithm, and develop a sum and difference beamspace domain refined maximum likelihood (SDBDRML) algorithm. Compared with the SDBDML algorithm, the SDBDRML algorithm has higher estimation accuracy and lower computational burden, although it is sensitive to antenna height and reflection coefficient errors. The theoretical target elevation angle root mean square error (RMSE) and the computational complexity of proposed algorithms are analyzed. Finally, our computer simulations and real data processing results demonstrate the effectiveness of the proposed algorithms.

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Beamspace U-ESPRIT DOA Estimation Algorithm of Coherently Distributed Sources in Massive MIMO Systems

Cited by 8 publications

References 12 publications

DOA Estimation Algorithm for Reconfigurable Intelligent Surface Co-Prime Linear Array Based on Multiple Signal Classification Approach

DOA Estimation Algorithm for Reconfigurable Intelligent Surface Co-Prime Linear Array Based on Multiple Signal Classification Approach

Low-Complexity One-Bit DOA Estimation for Massive ULA with a Single Snapshot

Sum and Difference Beamspace Domain Refined Maximum Likelihood Algorithm for Elevation Estimation in Multipath Environment

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