DOA Estimation and Mutual Coupling Calibration Algorithm for Array in Plasma Environment

Qin, Lihua; Li, Chenlu; Du, Ying; Li, Baoshan

doi:10.1109/tps.2020.2987041

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…It has been demonstrated that such array perturbations can degrade the estimator's performance substantially [32]. Although there have been some array calibration methods available, such as the eigenstructure-based method [33], the active calibration method [34], and the covariance difference based method [35], most of them are based on the point source model, uniform linear array and subspace theory, and it is very difficult to extend them to deal with the scenario considered in this work. Fortunately, based on the characteristics of DL, this issue can be tackled by enhancing the constructed D1D-CNN in the following two aspects: i) Transfer learning (TL) is used to promote the generalization ability of already trained D1D-CNN with a small amount of perturbed array data, which aims to align the features across array perturbations and reduce the distribution divergence.…”

Section: Enhanced D1d-cnn For Doa Estimation With Array Perturbationsmentioning

confidence: 99%

Vehicle Positioning With Deep-Learning-Based Direction-of-Arrival Estimation of Incoherently Distributed Sources

Tian

Liu

et al. 2022

IEEE Internet Things J.

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Section: Enhanced D1d-cnn For Doa Estimation With Array Perturbationsmentioning

confidence: 99%

Vehicle Positioning With Deep-Learning-Based Direction-of-Arrival Estimation of Incoherently Distributed Sources

Tian

Liu

et al. 2022

IEEE Internet Things J.

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“…The coefficient of mutual coupling is estimated using the Lagrange method. For the sake of brevity and since the analysis is not our contribution to knowledge, readers are referred to [45] for the detailed analysis and explanation.…”

Section: Doa Correction Algorithm For Joint Mutual Coupling Errormentioning

confidence: 99%

An Intelligent Deep Learning-Based Direction-of-Arrival Estimation Scheme Using Spherical Antenna Array With Unknown Mutual Coupling

Famoriji

Ogundepo

2020

IEEE Access

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Spherical antenna array (SAA) has become highly attractive where hemispherical scan coverage is required as it can provide uniform directivity in all the scan directions. Various direction-ofarrival (DoA) estimation methods suffer from different problems, such as low accuracies in mismatched conditions, high computational complexity and poor estimation in a harsh environment. Another critical concern is mutual coupling (MC) characteristics between the array elements. These problems affect the quality of the navigation signal in harsh environments. This paper presents a robust DoA estimation and mutual coupling compensation technique based on convolutional neural network (CNN) for Spherical Array. Spherical harmonic decomposition (SHD) is used to facilitate feature extraction in two sets, which contains different features about the elevation and azimuth of the source for DoA estimation. The features serve as input to the learning technique for separate estimation of elevation and azimuth, which consequently reduce computational complexity as against the joint estimation of DoA. Learning methods for DoA estimation with few frames and dense search grids within the spherical array configuration are presented. To solve the MC error, the DoA estimation scheme is also used to obtain accurate spectrum peak in the multipath scenario with unknown MC and sharper spectrum peak via the unique structure of the MC matrix and spatial smoothing algorithms. In all, experimental results, which is the ground truth to test any procedure, show the effectiveness, validity, and potential practical application of the proposed technique.

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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.…”

Section: Introductionmentioning

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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DOA Estimation and Mutual Coupling Calibration Algorithm for Array in Plasma Environment

Cited by 5 publications

References 21 publications

Vehicle Positioning With Deep-Learning-Based Direction-of-Arrival Estimation of Incoherently Distributed Sources

Vehicle Positioning With Deep-Learning-Based Direction-of-Arrival Estimation of Incoherently Distributed Sources

An Intelligent Deep Learning-Based Direction-of-Arrival Estimation Scheme Using Spherical Antenna Array With Unknown Mutual Coupling

An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning

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