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SDOA-Net: An Efficient Deep-Learning-Based DOA Estimation Network for Imperfect Array
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DOI：10.1109/TIM.2024.3391338.png)
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The estimation of direction of arrival (DOA) is a crucial issue in conventional radar, wireless communication, and integrated sensing and communication (ISAC) systems. However, low-cost systems often suffer from imperfect factors, such as antenna position perturbations, mutual coupling effect, inconsistent gains/phases, and nonlinear amplifier effect, which can significantly degrade the performance of DOA estimation. This article proposes a DOA estimation method named super-resolution DOA network (SDOA-Net) based on deep learning (DL) to characterize the realistic array more accurately. Unlike existing DL-based DOA methods, SDOA-Net uses sampled received signals instead of covariance matrices as input to extract data features. Furthermore, SDOA-Net produces a vector that is independent of the DOA of the targets but can be used to estimate their spatial spectrum. Consequently, the same training network can be applied to any number of targets, reducing the complexity of implementation. The proposed SDOA-Net with a low-dimension network structure also converges faster than existing DL-based methods. The simulation results demonstrate that SDOA-Net outperforms existing DOA estimation methods for imperfect arrays. The SDOA-Net code is available online at https://github.com/chenpengseu/SDOA-Net.git.
Keyword:
Direction-of-arrival estimation
Estimation
Covariance matrices
Vectors
Superresolution
Mutual coupling
Convolutional neural networks
Convolution layer
deep learning (DL)
direction of arrival (DOA) estimation
imperfect array
super-resolution method
期刊
IF:
5.9
论文数:
1.9W
被引数:
5.8W
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引用论文
分享收藏A PARAFAC Decomposition Algorithm for DOA Estimation in Colocated MIMO Radar With Imperfect Waveforms.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Single Snapshot Super-Resolution DOA Estimation for Arbitrary Array Geometries任意阵列几何形状的单快照超分辨率DOA估计OAAI
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