Self-Calibration of Mutual Coupling for Non-uniform Cross-Array

Zhang, Jia-Jia; Chen, Hui; Huang, Weiping; Liu, Weijian

doi:10.1007/s00034-018-0899-0

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…The algorithms in [38]- [47] are only suitable for a uniform linear array (ULA). Similar works have been done for uniform circular arrays [48]- [50], uniform rectangular arrays [51], [52], and cross arrays [53].…”

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confidence: 74%

Auxiliary Vehicle Positioning Based on Robust DOA Estimation With Unknown Mutual Coupling

Wen

Wang

Shi

et al. 2020

IEEE Internet Things J.

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As an important branch of the Internet of Vehicles (IoV), vehicle positioning has drawn extensive attention. Traditional positioning systems based on a global positioning system incur long delays, and may fail due to obstructions. In this article, we propose an auxiliary positioning architecture, whose core is to estimate the direction of arrival (DOA) of signals from landmarks, such as wireless access points, utilizing a sensor array in the vehicle. Due to space limitations, the array may be placed in an arbitrary geometry and may suffer from unknown mutual coupling. Most algorithms are only effective for sensor arrays with special geometries, e.g., a uniform linear array or rectangular array. To tackle this problem, an improved multiple signal classification algorithm is derived, which is superior to the state-of-the-art iterative method from the perspective of computational complexity. Detailed analysis concerning identifiability, computational complexity, and Cramér-Rao bounds are given. The simulation results verify the improvement of the proposed DOA estimation algorithm. The proposed architecture can obtain robust self-localization with existing vehicular ad hoc networks, and it can collaborate with other positioning systems to provide a safe driving environment. Index Terms-Arbitrary geometry, direction-of-arrival (DOA) estimation, Internet of Vehicles (IoV), mutual coupling, sensor array, vehicle positioning. I. INTRODUCTION R ECENT decades have witnessed explosive growth in the demands on the Internet of Vehicles (IoV) [1]-[8]. Generally speaking, the IoV refers to the infrastructure that connects vehicles to intervehicle networks [9]-[12], intravehicle networks, and the vehicular mobile Internet. The IoV is a complex system that integrates vehicle technology with Manuscript

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confidence: 74%

Auxiliary Vehicle Positioning Based on Robust DOA Estimation With Unknown Mutual Coupling

Wen

Wang

Shi

et al. 2020

IEEE Internet Things J.

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“…One is the active calibration method, wherein the error perturbation parameters are estimated offline by setting up an auxiliary signal source with precisely known direction in space [3][4][5]. The other is a self-calibration method based on some optimization functions for the joint estimation of the spatial orientation of the signal source and the perturbation error parameters of the array [6][7][8][9][10]. All these calibration methods deal with the actual signal and try to eliminate various error factors in the signal.…”

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confidence: 99%

An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning

et al. 2022

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Affected by various error factors in the actual environment, the accuracy of the direction of arrival (DOA) estimation algorithm will greatly decrease during an application. To address this issue, in this paper, we propose an intelligent DOA estimation error calibration method based on transfer learning, which learns error knowledge from a small number of actual signal samples and improves the DOA estimation accuracy in the real application. We constructed a deep convolutional neural network (CNN)-based intelligent DOA estimation model to learn the mapping between the input signals and their azimuths. We generated a large number of ideal simulation signal samples to train the CNN model and used it as the pretrained model. Then, we fine-tuned the CNN model with a small number of actual signal samples collected in the actual environment. We demonstrate the effectiveness of the proposed method through simulation experiments. The experimental results indicate that the proposed method can effectively improve the accuracy of DOA estimation in the actual environment.

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UNet-rootMUSIC: A high accuracy direction of arrival estimation method under array imperfection

Nguyen,

Le,

Doan

et al. 2024

AEU - International Journal of Electronics and Communications

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Self-Calibration of Mutual Coupling for Non-uniform Cross-Array

Cited by 4 publications

References 11 publications

Auxiliary Vehicle Positioning Based on Robust DOA Estimation With Unknown Mutual Coupling

Auxiliary Vehicle Positioning Based on Robust DOA Estimation With Unknown Mutual Coupling

An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning

UNet-rootMUSIC: A high accuracy direction of arrival estimation method under array imperfection

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