An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning

Zhang, Min; Wang, Chenyang; Zhu, Wenli; Shen, Yi

doi:10.3390/app12157636

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…ZLPR Loss compares all non-target class scores with the target class scores, in order to achieve the effect that the target class score is greater than the score of each non-target class. The specific formula is provided as follows: Loss = log 1 + ∑ i∈Ω neg e s i + log 1 + ∑ i∈Ω pos e −s i , (22) where Ω neg is the non-target class set, Ω pos is the target class set and S i is the score.…”

Section: Simulation and Discussionmentioning

confidence: 99%

“…In order to solve this problem, many scholars have introduced neural networks into the field of DOA estimation [ 19 , 20 , 21 , 22 , 23 ] and have achieved angle estimation by learning the nonlinear relationship between the output of the array and the position of the spatial signal source. Deep learning has been adopted for DOA estimation in large-scale MIMO arrays [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

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Direction-of-Arrival Estimation for a Random Sparse Linear Array Based on a Graph Neural Network

Yang,

Zhang,

Peng

et al. 2023

Sensors

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This article proposes a direction-of-arrival (DOA) estimation algorithm for a random sparse linear array based on a novel graph neural network (GNN). Unlike convolutional layers and fully connected layers, which do not interact well with information between different antennas, the GNN model can adapt to the goniometry problem of non-uniform random sparse linear arrays without any prior information by applying neighbor nodes’ aggregation and update operations. This helps the model in learning signal features under complex environmental conditions. We train the model in an end-to-end way to reduce the complexity of the network. Experiments are conducted on the uniform and sparse linear arrays for various signal-to-noise ratio (SNR) and numbers of snapshots for comparison. We prove that the GNN model has superior angle estimation performance on arrays with large sparsity that cannot be used by traditional algorithms and surpasses existing deep learning models based on convolutional or fully connected structures. The proposed algorithm shows excellent DOA estimation performance under the complex conditions of limited snapshots, low signal-to-noise ratio, and large array sparsity as well. In addition, the algorithm has a low time calculation cost and is suitable for scenarios that require low latency.

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Section: Simulation and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Direction-of-Arrival Estimation for a Random Sparse Linear Array Based on a Graph Neural Network

Yang,

Zhang,

Peng

et al. 2023

Sensors

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“…To verify the performance and overfitting of the developed learning model, new limited view data that were not used for learning were created and used. The third paper, authored by Zhang et al [18], proposed and evaluated an intelligent direction of arrival (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 a real application. In this article, a deep-CNN-based intelligent DOA estimation model to learn the mapping between the input signals and their azimuths was constructed.…”

Section: Future Information and Communication Engineering 2022mentioning

confidence: 99%

Current Research in Future Information and Communication Engineering 2022

Kim

et al. 2023

Applied Sciences

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“…Now, the 2-D direction of arrival estimation problem is widely used in radar, internet of vehicle (IOV) and the fifth-generation (5 G) mobile communications [ 1 , 2 , 3 , 4 , 5 , 6 ]. And many algorithms have been developed to solve the problem of DOA estimation, such as improved reduced dimension MUSIC (IRD-MUSIC), the reduced-dimension multiple signal classification algorithm, and so on [ 7 , 8 , 9 , 10 , 11 , 12 ]. Compared to a 2-D planar array, L-shaped sparse arrays have lower costs and better adaptability.…”

Section: Introductionmentioning

confidence: 99%

An L-Shaped Three-Level and Single Common Element Sparse Sensor Array for 2-D DOA Estimation

Cui

et al. 2023

Sensors

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The degree of freedom (DOF) is an important performance metric for evaluating the design of a sparse array structure. Designing novel sparse arrays with higher degrees of freedom, while ensuring that the array structure can be mathematically represented, is a crucial research direction in the field of direction of arrival (DOA) estimation. In this paper, we propose a novel L-shaped sparse sensor array by adjusting the physical placement of the sensors in the sparse array. The proposed L-shaped sparse array consists of two sets of three-level and single-element sparse arrays (TSESAs), which estimate the azimuth and elevation angles, respectively, through one-dimensional (1-D) spatial spectrum search. Each TSESA is composed of a uniform linear subarray and two sparse subarrays, with one single common element in the two sparse subarrays. Compared to existing L-shaped sparse arrays, the proposed array achieves higher degrees of freedom, up to 4Q1Q2+8Q1−5, when estimating DOA using the received signal covariance. To facilitate the correct matching of azimuth and elevation angles, the cross-covariance between the two TSESA arrays is utilized for estimation. By comparing and analyzing performance parameters with commonly used L-shaped and other sparse arrays, it is found that the proposed L-shaped TSESA has higher degrees of freedom and array aperture, leading to improved two-dimensional (2-D) DOA estimation results. Finally, simulation experiments validate the excellent performance of the L-shaped TSESA in 2-D DOA estimation.

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An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning

Cited by 5 publications

References 27 publications

Direction-of-Arrival Estimation for a Random Sparse Linear Array Based on a Graph Neural Network

Direction-of-Arrival Estimation for a Random Sparse Linear Array Based on a Graph Neural Network

Current Research in Future Information and Communication Engineering 2022

An L-Shaped Three-Level and Single Common Element Sparse Sensor Array for 2-D DOA Estimation

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