Source localization using a sparse representation framework to achieve superresolution

Guo, Xiansheng; Wan, Qun; Chang, Chunqi; Lam, Edmund Y.

doi:10.1007/s11045-010-0119-y

Cited by 20 publications

(9 citation statements)

References 28 publications

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“…The key lies in its high‐resolution capability in DOA estimation. Actually, DOA estimation has been implemented for the MMV model (2) in [10–12] by second‐order cone programming. The work in [10] is a special case of [12].…”

Section: Simulationmentioning

confidence: 99%

“…Sparse recovery [8, 9] has attracted a lot of attention and it has been utilised in many fields. One of them is source localisation [10–12] which obtains higher angular resolution capability than previous subspace‐based methods. The attention of sparse recovery is mainly focused on two kinds of problems, which are the single measurement vector (SMV) problem and the multiple measurement vector (MMV) problem.…”

Section: Introductionmentioning

confidence: 99%

“…When convex optimisation is used to search the non‐zero elements of the sparse vector

bold-italicα

in (1) and the non‐zero rows of the matrix

bold-italicP

in (2), the 0‐norm problem is replaced by the 1‐norm problem. In [13–16], (1) is formulated as

min {∥bold-italicα∥}_{1} normals . t . (∥∥, bold-italics - boldΦ bold-italicα) \leq δ

In [12], (2) is formulated as

min {∥\tilde{P}∥}_{2, 1} normals . t . {∥X - Φ P∥}_{F} \leq β

The authors in [10–12] have utilised the convex optimisation method to find directions‐of‐arrival (DOAs) of incident signals. The non‐zero rows of

bold-italicP

correspond to the angles of incident signals.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discrimination and identification between mainlobe repeater jamming and target echo via sparse recovery

Ding

Li²,

Dai³

et al. 2017

IET Radar, Sonar & Navigation

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Section: Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…When convex optimisation is used to search the non‐zero elements of the sparse vector

bold-italicα

in (1) and the non‐zero rows of the matrix

bold-italicP

in (2), the 0‐norm problem is replaced by the 1‐norm problem. In [13–16], (1) is formulated as

min {∥bold-italicα∥}_{1} normals . t . (∥∥, bold-italics - boldΦ bold-italicα) \leq δ

In [12], (2) is formulated as

min {∥\tilde{P}∥}_{2, 1} normals . t . {∥X - Φ P∥}_{F} \leq β

The authors in [10–12] have utilised the convex optimisation method to find directions‐of‐arrival (DOAs) of incident signals. The non‐zero rows of

bold-italicP

correspond to the angles of incident signals.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Discrimination and identification between mainlobe repeater jamming and target echo via sparse recovery

Ding

Li²,

Dai³

et al. 2017

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

“…For example, a corresponding transmitting or receiving beamformer can be designed to extract signals in the direction of interest and suppress uninteresting interference signals. The electromagnetic vector sensor (EMVS) can catch polarization-related information compared to a conventional scalar sensor, which can further improve the target resolution, anti-interference ability and detection stability for DOA estimation [ 5 , 6 , 7 ]; therefore, the research for EMVS array direction finding has become a hotspot.…”

Section: Introductionmentioning

confidence: 99%

Geometric Algebra-Based ESPRIT Algorithm for DOA Estimation

Wang

et al. 2021

Sensors

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Direction-of-arrival (DOA) estimation plays an important role in array signal processing, and the Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm is one of the typical super resolution algorithms for direction finding in an electromagnetic vector-sensor (EMVS) array; however, existing ESPRIT algorithms treat the output of the EMVS array either as a “long vector”, which will inevitably lead to loss of the orthogonality of the signal components, or a quaternion matrix, which may result in some missing information. In this paper, we propose a novel ESPRIT algorithm based on Geometric Algebra (GA-ESPRIT) to estimate 2D-DOA with double parallel uniform linear arrays. The algorithm combines GA with the principle of ESPRIT, which models the multi-dimensional signals in a holistic way, and then the direction angles can be calculated by different GA matrix operations to keep the correlations among multiple components of the EMVS. Experimental results demonstrate that the proposed GA-ESPRIT algorithm is robust to model errors and achieves less time complexity and smaller memory requirements.

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“…However, CF-LCMV beamformer requires inversions of the input data covariance matrix R xx and C H C (C is a constraint matrix), causing the main computation burden of CF-LCMV algorithm. Additionally, the robustness of CF-LCMV is poor when small number of snapshots, low SNR and JNRs, are present [16].…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive LCMV Beamformer Based On An Iterative Suboptimal Solution

Chu¹,

Li²,

Guo³

2015

RADIOENGINEERING

Self Cite

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Abstract. The main drawback of closed-form solution of linearly constrained minimum variance (CF-LCMV) beamformer is the dilemma of acquiring long observation time for stable covariance matrix estimates and short observation time to track dynamic behavior of targets, leading to poor performance including low signal-noise-ratio (SNR), low jammer-to-noise ratios (JNRs) and small number of snapshots. Additionally, CF-LCMV suffers from heavy computational burden which mainly comes from two matrix inverse operations for computing the optimal weight vector. In this paper, we derive a low-complexity Robust Adaptive LCMV beamformer based on an Iterative Suboptimal solution (RAIS-LCMV) using conjugate gradient (CG) optimization method. The steepest descent weight updated strategy is adopted to obtain a simple iteration process. The merit of our proposed method is threefold. Firstly, RAIS-LCMV beamformer can reduce the complexity of CF-LCMV remarkably. Secondly, RAIS-LCMV beamformer can adjust output adaptively based on measurement and its convergence speed is comparable. Finally, RAIS-LCMV algorithm has robust performance against low SNR, JNRs, and small number of snapshots. Simulation results demonstrate the superiority of our proposed algorithms. KeywordsRobust adaptive beamformer, LCMV, iterative suboptimal solution, computational analysis

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Source localization using a sparse representation framework to achieve superresolution

Cited by 20 publications

References 28 publications

Discrimination and identification between mainlobe repeater jamming and target echo via sparse recovery

Discrimination and identification between mainlobe repeater jamming and target echo via sparse recovery

Geometric Algebra-Based ESPRIT Algorithm for DOA Estimation

Robust Adaptive LCMV Beamformer Based On An Iterative Suboptimal Solution

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