Research on Real-Time 2-D DOA Estimation Algorithm for Uniform Circular Array

Rui, R.; Zhang, Xiongkui

doi:10.1109/icet51757.2021.9451135

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…They are not suitable for multi-source scenarios where there are only hundreds of snapshots in the simulation. Autocalibration techniques [13][14][15][16][17][18] are not introduced into the parametric method because there is no prior information about array imperfections.…”

Section: Simulation and Analysismentioning

confidence: 99%

“…Therefore, in actual signal estimation, these imperfections will have a great impact on performance, resulting in a decrease in estimation accuracy [11,12]. Many methods simplify the model to describe the effects of various imperfections and propose an automatic calibration process to improve the accuracy of DOA estimation [13][14][15][16][17][18][19]. The simplification of the array error is carried out from a mathematical point of view, with various additional assumptions, such as uniform linear/circular array geometry [13][14][15], sensors being constrained in a specific straight line or plane [16], position error [17], and the intersensor independence of gain and phase error [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…Many methods simplify the model to describe the effects of various imperfections and propose an automatic calibration process to improve the accuracy of DOA estimation [13][14][15][16][17][18][19]. The simplification of the array error is carried out from a mathematical point of view, with various additional assumptions, such as uniform linear/circular array geometry [13][14][15], sensors being constrained in a specific straight line or plane [16], position error [17], and the intersensor independence of gain and phase error [18,19]. These simplifications and assumptions deviate from reality to varying degrees, and how to deal with array imperfections is still an open problem.…”

Section: Introductionmentioning

confidence: 99%

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Robust direction‐of‐arrival estimation approach using beamspace‐based deep neural networks with array imperfections and element failure

Wen

Huang

et al. 2022

IET Radar Sonar & Navi

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Direction-of-arrival (DOA) estimation is a fundamental functionality of sensor array systems. Previous methods only consider element failure or array imperfections. In fact, the co-existence of element failure and array imperfections is more in line with the realistic operation conditions of an array system. However, the performance of previous methods degrades significantly in the presence of both array imperfections and element failure. To deal with this problem, a robust DOA estimation method is proposed based on a deep neural network (DNN). The proposed DNN consists of a denoising autoencoder (DAE) and a parallel network. The DAE can restore damaged array signals to "non-corrupted" signals, and the parallel network is able to process signals with different levels of loss to improve DOA estimation accuracy. At the training stage, array imperfections are modelled as a spherical distribution, and the training samples are extracted under this distribution to improve the generalisation capability of the proposed network to various array imperfections. In addition, the authors propose to estimate the spatial spectrum in a virtual array beamspace, which can reduce the computational complexity as well as signal-to-noise ratio resolution threshold, and further enhance the robustness to array imperfections. Numerical results show that the proposed DOA estimation approach works well in the presence of both array imperfections and element failure.

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Section: Simulation and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust direction‐of‐arrival estimation approach using beamspace‐based deep neural networks with array imperfections and element failure

Wen

Huang

et al. 2022

IET Radar Sonar & Navi

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“…DOA estimation extracting spatial characteristics through the phase of the received signal directly, called Correlative Interferometer (CI), is also being actively investigated in the literature thanks to its good performance, computational efficiency, high flexibility, and low-complexity implementation [19][20][21][22][23][24][25]. In [19], an antenna-array cascade system was proposed to improve DOA estimation performance by resolving DOA ambiguity due to the small number of antenna elements.…”

Section: Introductionmentioning

confidence: 99%

“…In [19], an antenna-array cascade system was proposed to improve DOA estimation performance by resolving DOA ambiguity due to the small number of antenna elements. In [20], a low-complexity CI algorithm using a modified cost function for real-time two-dimensional DOA estimation with uniform circular array (UCA) was proposed. In [21], a time-modulated array-based DOA estimation technique was proposed for improving the accuracy of direction finding.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Correlative Interferometer Technique for Direction Finding of Signal Sources

Oh,

Lee,

Lee

et al. 2023

Sensors

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In this paper, we propose a novel simultaneous Correlative Interferometer (CI) technique that elaborately estimates the Direction of Arrival (DOA) of multiple source signals incident on an antenna array. The basic idea of the proposed technique is that the antenna-array-based receiver compares the phase of the received signal with one of the candidates at each time sample and jointly exploits these multiple time samples to estimate the DOAs of multiple signal sources. The proposed simultaneous CI-based DOA estimation technique collectively utilizes multiple time-domain samples and can be regarded as a generalized version of the conventional CI algorithm for the case of multiple time-domain samples. We first thoroughly review the conventional CI algorithm to comprehensively explain the procedure of the direction-finding algorithm that adopts the phase information of received signals. We also discuss several technical issues of conventional CI-based DOA estimation techniques that are originally proposed for the case of a single time-domain sample. Then, we propose a simultaneous CI-based DOA estimation technique with multi-sample diversity as a novel solution for the case of multiple time-domain samples. We clearly compare the proposed simultaneous CI technique with the conventional CI technique and we compare the existing Multiple Signal Classification (MUSIC)-based DOA estimation technique with the conventional CI-based technique by using the DOA spectrum as well. To the best of our knowledge, the simultaneous CI-based DOA estimation technique that effectively utilizes the characteristics of multiple signal sources over multiple time-domain samples has not been reported in the literature. Through extensive computer simulations, we show that the proposed simultaneous CI technique significantly outperforms both the conventional CI technique in terms of DOA estimation even in harsh environments and with various antenna array structures. It is worth noting that the proposed simultaneous CI technique results in much better performance than the classical MUSIC algorithm, which is one of the most representative subspace-based DOA estimation techniques.

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Direction of Arrival Estimation Based on Improved Measurement Matrix for Uniform Circle Array

Sun,

Wei,

Wang

et al. 2023

2023 International Conference on Microwave and Millimeter Wave Technology (ICMMT)

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Research on Real-Time 2-D DOA Estimation Algorithm for Uniform Circular Array

Cited by 4 publications

References 12 publications

Robust direction‐of‐arrival estimation approach using beamspace‐based deep neural networks with array imperfections and element failure

Robust direction‐of‐arrival estimation approach using beamspace‐based deep neural networks with array imperfections and element failure

Simultaneous Correlative Interferometer Technique for Direction Finding of Signal Sources

Direction of Arrival Estimation Based on Improved Measurement Matrix for Uniform Circle Array

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