Geometric Algebra-Based ESPRIT Algorithm for DOA Estimation

Wang, Rui; Wang, Yue; Li, Yanping; Cao, Wenming; Yan, Yi

doi:10.3390/s21175933

Cited by 13 publications

(14 citation statements)

References 41 publications

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“…Several solutions have been proposed to obtain DoA information, e.g., beam scanning [ 21 ] or super-resolution methods [ 22 , 23 ]. Nevertheless, we assume that the angular direction of the target is known, as these estimation methods fall out of the scope of the present work.…”

Section: Introductionmentioning

confidence: 99%

Beamforming Techniques for Passive Radar: An Overview

Núñez-Ortuño¹,

González-Coma²,

López³

et al. 2023

Sensors

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Passive radar is an interesting approach in the context of non-cooperative target detection. Because the signal source takes advantage of the so-called illuminator of opportunity (IoO), the deployed system is silent, allowing the operator cheap, portable, and practically undetectable deployments. These systems match perfectly with the use of antenna arrays to take advantage of the additional gains provided by the coherent combination of the signals received at each element. To obtain these benefits, linear processing methods are required to enhance the system’s performance. In this work, we summarize the main beamforming methods in the literature to provide a clear picture of the current state of the art. Next, we perform an analysis of the benefits and drawbacks and explore the chance of increasing the number of antenna elements. Finally, we identify the major challenges to be addressed by researchers in the future.

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Section: Introductionmentioning

confidence: 99%

Beamforming Techniques for Passive Radar: An Overview

Núñez-Ortuño¹,

González-Coma²,

López³

et al. 2023

Sensors

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“…It is the critical technology of array signal processing, and the purpose is to estimate the transmitter positions of the signals received by arrays. e representative conventional DOA estimation algorithms are the estimation of signal parameters via rotational invariance techniques (ESPRIT) [4,5] and multiple signal classification (MUSIC) [6,7]. ESPRIT uses two subarrays with translation invariance to realize DOA estimation.…”

Section: Introductionmentioning

confidence: 99%

Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks

Chen

Joo

2022

International Journal of Antennas and Propagation

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This work proposes an effective high-resolution multisource direction-of-arrival (DOA) estimation method in impulsive noise scenarios based on convolutional neural networks (CNNs). First of all, the array observation matrix is preprocessed and fed into a denoising network to suppress outliers and filter out impulsive noise. Secondly, the denoising network output is fed into a model order selection network to estimate the model order. Next, according to the estimation, the denoising network output is fed into a DOA subnetwork corresponding to the model order in a DOA network to estimate the DOA of each signal. Comprehensive simulations demonstrate that, in the presence of impulsive noise, the proposed method is effective and superior in accuracy and computation speed for multisource DOA estimation. Therefore, it is concluded that CNN can be well generalized for DOA estimation.

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“…Traditional sparse representation fusion method usually processes the color channels separately, which easily destroys the correlation between image channels and results in loss of color in the fused image. Geometric algebra (GA) has been considered as one of the most powerful tools in multi-dimensional signal processing and has witnessed great success in a wide range of applications, such as physics, quantum computing, electromagnetism, satellite navigation, neural computing, camera geometry, image processing, robotics, and computer vision, et al ( da Rocha and Vaz, 2006 ; Wang et al, 2019a ; Wang et al, 2021a ). Inspired by the paper ( Wang et al, 2019b ), the geometric algebra-based sparse representation (GA-SR) is introduced for multi-modal medical image fusion in this paper.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Medical Image Fusion With Geometric Algebra Based Sparse Representation

Fang

Wang

et al. 2022

Front. Genet.

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Multi-modal medical image fusion can reduce information redundancy, increase the understandability of images and provide medical staff with more detailed pathological information. However, most of traditional methods usually treat the channels of multi-modal medical images as three independent grayscale images which ignore the correlation between the color channels and lead to color distortion, attenuation and other bad effects in the reconstructed image. In this paper, we propose a multi-modal medical image fusion algorithm with geometric algebra based sparse representation (GA-SR). Firstly, the multi-modal medical image is represented as a multi-vector, and the GA-SR model is introduced for multi-modal medical image fusion to avoid losing the correlation of channels. Secondly, the orthogonal matching pursuit algorithm based on geometric algebra (GAOMP) is introduced to obtain the sparse coefficient matrix. The K-means clustering singular value decomposition algorithm based on geometric algebra (K-GASVD) is introduced to obtain the geometric algebra dictionary, and update the sparse coefficient matrix and dictionary. Finally, we obtain the fused image by linear combination of the geometric algebra dictionary and the coefficient matrix. The experimental results demonstrate that the proposed algorithm outperforms existing methods in subjective and objective quality evaluation, and shows its effectiveness for multi-modal medical image fusion.

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Geometric Algebra-Based ESPRIT Algorithm for DOA Estimation

Cited by 13 publications

References 41 publications

Beamforming Techniques for Passive Radar: An Overview

Beamforming Techniques for Passive Radar: An Overview

Multisource DOA Estimation in Impulsive Noise Environments Using Convolutional Neural Networks

Multi-Modal Medical Image Fusion With Geometric Algebra Based Sparse Representation

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