An Efficient Super-Resolution DOA Estimator Based on Grid Learning

Li, X.; Liu, Qi; Wang, W.; Wei, Zhenyu; Wang, B.

doi:10.13164/re.2019.0785

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…As mentioned, to address the basis mismatch, off-grid sparse method is also used. The signal model for the off-grid target with bias is given in [26,27]. The minimization problem can be written as min s,δ…”

Section: Simulation Resultsmentioning

confidence: 99%

A New Computing Paradigm for Off-Grid Direction of Arrival Estimation Using Compressive Sensing

Mirza

Aslam

Raja

et al. 2020

Wireless Communications and Mobile Computing

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In this paper, a method for solving grid mismatch or off-grid target is presented for direction of arrival (DOA) estimation problem using compressive sensing (CS) technique. Location of the sources are at few angles as compare to the entire angle domain, i.e., spatially sparse sources, and their location can be estimated using CS methods with ability of achieving super resolution and estimation with a smaller number of samples. Due to grid mismatch in CS techniques, the source energy is distributed among the adjacent grids. Therefore, a fitness function is introduced which is based on the difference of the source energy among the adjacent grids. This function provides the best discretization value for the grid through iterative grid refinement. The effectiveness of the proposed scheme is verified through extensive simulations for different number of sources.

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Section: Simulation Resultsmentioning

confidence: 99%

A New Computing Paradigm for Off-Grid Direction of Arrival Estimation Using Compressive Sensing

Mirza

Aslam

Raja

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Prior articles have proved that DOA estimation problem is a kind of convexity problem 22 , 23 , whose sparsity property can be solved by the -norm approximation 24 , 25 . Many convex optimization algorithms have been used in the DOA estimation field such as the -norm to approximatively solve the -norm constrained minimization problem and obtain the off-grid DOA estimation results 26 , and the low-rank matrix approximation method to recover the DOA information from the array observation matrix and a weakly convex function in LRMA 27 .…”

Section: Introductionmentioning

confidence: 99%

Multi-DOA estimation based on the KR image tensor and improved estimation network

Yuan

Yang

et al. 2021

Sci Rep

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Deep neural networks have shown great performance for direction-of-arrival (DOA) estimation problem, but it is necessary to design some suitable networks to solve the multi-DOA estimation problem. In this paper, we use Khatri–Rao product to increase the degree of freedom of antenna array and obtain the image tensor of covariance matrix, then we propose an improved estimation network to process the tensor. We use the curriculum learning scheme and partial label strategy to develop a CurriculumNet training scheme. The training/validation results shows that the proposed training scheme can increase the generalization of the estimation network and improve the accuracy of network around $$10\%$$ 10 % . The estimation performance of the proposed network shows high-resolution results, which can distinguish two adjacent signals with angle difference of $$3^\circ $$ 3 ∘ . Moreover, the proposed estimation network has root mean square estimation error lower than $$1^\circ $$ 1 ∘ when signal noise ratio equals $$-\,10\,{\mathrm {dB}}$$ - 10 dB and can estimate DOAs precisely by only 8 snapshots, which performs much better than prior deep neural network based estimation methods and can estimate multi-DOA results under hostile estimation environments.

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“…In recent years, various DOA estimation algorithms have been proposed, including the Capon algorithm, maximum likelihood (ML) algorithms, multiple signal classification (MUSIC) algorithm, and the estimation of parameters via the rotational invariance technique (ESPRIT). In addition, advanced with the recently developed sparse signal reconstruction (SSR) scheme, several DOA algorithms have been proposed by exploiting the spatial sparse property [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Self-Calibration Algorithm With Gain-Phase Errors Array for Robust Doa Estimation

Wei¹,

Wang²,

Dong³

et al. 2021

PIER M

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The performance of direction-of-arrival (DOA) estimation algorithms degrades when a partly calibrated array is adopted due to the existing unknown gain-phase uncertainties. In addition, the spatial discretized searching grid also limits the performance improvement and effectiveness of subspacebased DOA estimation algorithms, especially when the true angles do not lie on the grid points which is referred to the off-grid problem alike. In this paper, a self-calibration DOA estimation algorithm is proposed which solves the array calibration and off-grid problems simultaneously. Firstly, the signal model for a partly calibrated array with gain-phase uncertainties is established. To suppress the offgrid errors, an optimization problem for joint parameters estimation is constructed by substituting the approximation of the steering vector into a newly constructed objective function. The alternative minimization (AM) algorithm is employed to calculate the joint DOA and gain-phase uncertainty estimations. Within each iteration step of the optimization problem, a closed-form solution is derived that guarantees the convergence of the proposed algorithm. Furthermore, the Cramér-Rao bound (CRB) for the partly calibrated arrays with unknown gain-phase uncertainties is also derived and analyzed in the paper. Simulation results demonstrate the effectiveness of the proposed algorithm.

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An Efficient Super-Resolution DOA Estimator Based on Grid Learning

Cited by 7 publications

References 26 publications

A New Computing Paradigm for Off-Grid Direction of Arrival Estimation Using Compressive Sensing

A New Computing Paradigm for Off-Grid Direction of Arrival Estimation Using Compressive Sensing

Multi-DOA estimation based on the KR image tensor and improved estimation network

Self-Calibration Algorithm With Gain-Phase Errors Array for Robust Doa Estimation

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