A novel variational SBL approach for off-grid DOA detection under nonuniform noise

Wang, Huafei; Wang, Xianpeng; Huang, Mengxing; Wan, Liangtian

doi:10.1016/j.dsp.2022.103622

Cited by 6 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second one includes super-resolution goniometry algorithms based on subspace, such as estimating signal parameters via rotational invariance techniques (ESPRIT) [ 10 ], multi-signal classification (MUSIC) [ 11 ], and some of its derivatives [ 12 , 13 , 14 ]. The third one comprises sparse reconstruction algorithms, such as atomic norm minimization [ 15 ], sparse Bayesian learning (SBL) [ 16 ], compressed sensing (CS) [ 17 , 18 ] algorithms, etc. These algorithms rely on traditional array structures to achieve accurate DOA estimation with high SNR and many snapshots.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…[16,17] introduced different forms of real-value transformation, using compressed sensing technology to transform the data in the complex number field into the real number field and reduce the operation time of each iteration. Then, for different application backgrounds such as color noise and broadband signals, many researchers used the DOA estimation methods based on variational sparse Bayes [18][19][20], and the DOA estimation performances of such methods were further improved using SBL. Nevertheless, the above methods based on sparse Bayesian learning are all based on ULAs, which can only provide one-dimensional angle information.…”

Section: Introductionmentioning

confidence: 99%

Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors

Jin,

Wang,

Hou

et al. 2023

Sensors

View full text Add to dashboard Cite

To reduce the influence of gain-phase errors and improve the performance of direction-of-arrival (DOA) estimation, a robust sparse Bayesian two-dimensional (2D) DOA estimation method with gain-phase errors is proposed for L-shaped sensor arrays. The proposed method introduces an auxiliary angle to transform the 2D DOA estimation problem into two 1D angle estimation problems. A sparse representation model with gain-phase errors is constructed using the diagonal element vector of the cross-correlation covariance matrix of two submatrices of the L-shaped sensor array. The expectation maximization algorithm derives unknown parameter expression, which is used for iterative operations to obtain off-grid and signal precision. Using these parameters, a new spatial spectral function is constructed to estimate the auxiliary angle. The obtained auxiliary angle is substituted into a sparse representation model with gain and phase errors, and then the sparse Bayesian learning method is used to estimate the elevation angle of the incident signal. Finally, according to the relationship of the three angles, the azimuth angle can be estimated. The simulation results show that the proposed method can effectively realize the automatic matching of the azimuth and elevation angles of the incident signal, and improves the accuracy of DOA estimation and angular resolution.

show abstract

A gridless method for direction finding with sparse arrays in nonuniform noise

Gong

Zhong

Ren

et al. 2023

Digital Signal Processing

View full text Add to dashboard Cite

A novel variational SBL approach for off-grid DOA detection under nonuniform noise

Cited by 6 publications

References 26 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

Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors

A gridless method for direction finding with sparse arrays in nonuniform noise

Contact Info

Product

Resources

About