Gridless One-Bit Direction-of-Arrival Estimation Via Atomic Norm Denoising

Wei, Zhenyu; Wang, Wei; Dong, Fuwang; Liu, Qi

doi:10.1109/lcomm.2020.3000755

Cited by 46 publications

(23 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DP method transforms the optimal solution of the original problem to that of the dual problem. We can obtain the frequency support set and estimate the DOA [23,29].…”

Section: Admm For One-bit Multiple Snapshot Modelmentioning

confidence: 99%

“…In [23], the authors present that sign inconsistency are induced by one-bit measurements with additive noises and an atomic norm minimization with a linear loss function can overcome the shortage. Meanwhile, as a gridless method, atomic norm minimization can avoid the basis mismatches caused by discretization of the signal parameter space [24,25].…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, as a gridless method, atomic norm minimization can avoid the basis mismatches caused by discretization of the signal parameter space [24,25]. The alternating direction multiplier method (ADMM) is proposed to solve the atomic norm minimization with one-bit quantization in the single measurement vector (SMV) model [23,26,27]. ADMM has lower computational complexity and higher convergence speed compared with the semidefinite programming (SDP) [28].…”

Section: Introductionmentioning

confidence: 99%

“…The MMV model has a higher success rate to reconstruct unknown signals compared with that of the SMV model. And then, two DOA estimation methods based on the constructed denoising measurements are proposed, spatial smoothing root-MUSIC (SSRMUSIC) algorithm [22] and dual polynomial (DP) method [23,29]. In order to highlight the advantage of ADMM in signal reconstruction, SSRMUSIC algorithm with constructed denoising measurements will be compared with ordinary one-bit SSRMUSIC algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Gridless Multiple Measurements Method for One‐Bit DOA Estimation with a Nested Cross‐Dipole Array

Long

Wang

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The gridless one-bit direction of arrival (DOA) estimator is proposed to estimate electromagnetic (EM) sources on a nested cross-dipole array, and the multiple measurement vectors (MMV) mode is introduced to improve the reliability of parameter estimation. The gridless method is based on atomic norm minimization, solved by alternating direction multiplier method (ADMM). With gridless method used, sign inconsistency caused by one-bit measurements and basis mismatches by traditional grid-based algorithms can be avoided. Furthermore, the reconstructed denoising measurements with fast convergence and stable recovery accuracy are obtained by ADMM. Finally, spatial smoothing root multiple signal classification (SSRMUSIC) and dual polynomial (DP) methods are used, respectively, to estimate the DOAs on the reconstructed denoising measurements. Numerical results show that our method one-bit ADMM-SSRMUSIC has a better performance than that of one-bit SSRMUSIC used directly. At low signal to noise ratio (SNR) and low snapshot, the one-bit ADMM-DP has an excellent performance which is even better than that of unquantized MUSIC. In addition, the proposed methods are also suitable for both completely polarized (CP) signals and partially polarized (PP) signals.

show abstract

“…The DP method transforms the optimal solution of the original problem to that of the dual problem. We can obtain the frequency support set and estimate the DOA [23,29].…”

Section: Admm For One-bit Multiple Snapshot Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gridless Multiple Measurements Method for One‐Bit DOA Estimation with a Nested Cross‐Dipole Array

Long

Wang

et al. 2021

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

View full text Add to dashboard Cite

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.

show abstract

One-Bit Gridless DOA Estimation with Multiple Measurements Exploiting Accelerated Proximal Gradient Algorithm

Tang

Jiang

2021

Circuits Syst Signal Process

View full text Add to dashboard Cite

Gridless One-Bit Direction-of-Arrival Estimation Via Atomic Norm Denoising

Cited by 46 publications

References 21 publications

Gridless Multiple Measurements Method for One‐Bit DOA Estimation with a Nested Cross‐Dipole Array

Gridless Multiple Measurements Method for One‐Bit DOA Estimation with a Nested Cross‐Dipole Array

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

One-Bit Gridless DOA Estimation with Multiple Measurements Exploiting Accelerated Proximal Gradient Algorithm

Contact Info

Product

Resources

About