DOA Estimation of Strictly Noncircular Sources in Wireless Sensor Array Network via Block Sparse Representation

Li, Liangliang; Fu, Tingting; Wang, Xianpeng; Huang, Mengxing; Wan, Liangtian; Yang, Yongqin

doi:10.1109/access.2020.2979055

Cited by 6 publications

(12 citation statements)

References 31 publications

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“…, y M (t)] T ∈ C M×1 represents the actual received data disturbed by unknown mutual coupling, unlike x(t) in (1). According to the above introduction, s d (t) denotes the noncircular source, which means that its noncircular rate ξ ranges from 0 to 1, including the upper limit [38]. When referring to the maximum noncircular rate ξ � 1, the radiation signal can be defned as the strictly noncircular source, like AM modulation signal.…”

Section: Data Model For Doa Estimationmentioning

confidence: 99%

“…Tis perturbation leads to undesired array manifold, thereby degrading or even invalidating the estimation performance of these approaches. Afterwards, a large number of calibration ideas are designed to deal with the problem of unknown mutual coupling [27][28][29][30][31][32][33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

“…For another, some studies [35][36][37][38][39][40] on noncircular sources have been done to estimate DOAs. In [35], a selection matrix is frst constructed to remove the negative infuence so as to directly apply ESPRIT principle.…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods are still subject to the limitations of subspace technology, unlike robust SSR algorithms. In view of this shortcoming, the joint reweighted sparsity-inducing scheme based on SVD principle [37] and WSF principle [38] are put forward, respectively. Whereas, their computational complexity is relatively higher than that of subspace-based methods in [35,36].…”

Section: Introductionmentioning

confidence: 99%

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Real‐Valued Weighted Subspace Fitting Algorithm for DOA Estimation with Block Sparse Recovery

Wang

Shi³

et al. 2023

Mathematical Problems in Engineering

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In this paper, the problem of direction-of-arrival (DOA) estimation for strictly noncircular sources under the condition of unknown mutual coupling is concerned, and then a robust real-valued weighted subspace fitting (WSF) algorithm is proposed via block sparse recovery. Inspired by noncircularity, the real-valued coupled extended array output with double array aperture is first structured via exploiting the real-valued conversion. Then, an efficient real-valued block extended sparse recovery model is constructed by performing the parameterized decoupling operation to avoid the unknown mutual coupling and noncircular phase effects. Thereafter, the WSF framework is investigated to recover the real-valued block sparse matrix, where the spectrum of real-valued NC MUSIC-like is utilized to design a weighted matrix for strengthening the solutions sparsity. Eventually, DOA estimation is achieved based on the support set of the reconstructed block sparse matrix. Owing to the combination of noncircularity, parametrized decoupling thought, and reweighted strategy, the proposed method not only effectively achieves high-precision estimation, but also efficiently reduces the computational complexity. Plenty of simulation results demonstrate the effectiveness and efficiency of the proposed method.

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Section: Data Model For Doa Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real‐Valued Weighted Subspace Fitting Algorithm for DOA Estimation with Block Sparse Recovery

Wang

Shi³

et al. 2023

Mathematical Problems in Engineering

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“…The block SR brings a new perspective to the SR-STAP. Although block SR has been introduced in many applications [22][23][24], to the best of our knowledge, block SR-STAP has not been reported in the literature so far. In this study, we mainly consider introducing the block structure of the clutter into the conventional SR-STAP to further improve the ability of clutter suppression.…”

Section: Introductionmentioning

confidence: 99%

Knowledge‐aided block sparse Bayesian learning STAP for phased‐array MIMO airborne radar

Cui

Xing

Duan

et al. 2021

IET Radar Sonar & Navi

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The phased-array multiple-input multiple-output (PA-MIMO) airborne radar faces more severe sample shortage problem than the conventional PA radar. Hence, it suffers from severe performance degradation when it adopts the traditional space-time adaptive processing (STAP) methods. Fortunately, the introduction of sparse recovery (SR) brings a novel perspective to reducing the sample requirement. However, most existing SR-STAP methods only consider the clutter sparsity as prior information. In fact, more useful information, such as radar parameters, ground environment, airplane state, can all be obtained in advance and further utilised to improve the performance of SR-STAP. In this study, the clutter structure, obtained based on the abovementioned prior knowledge, is utilised in the block SR-STAP in the PA-MIMO airborne radar. The clutter suppression performance is poor when the block SR algorithm is directly applied into the STAP because the clutter structure is destroyed by common vectorisation operation. To solve this problem, the vectorisation operation is first redesigned based on the clutter distribution characteristics and a novel dictionary is constructed. A basic block sparse Bayesian learning STAP scheme is then proposed in the PA-MIMO radar's application. The numerical experiments illustrate that the proposed method can achieve satisfactory performance with a few training samples and outperforms the state-of-the-art SR-STAP methods.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Sparsity-based DOA Estimation of 2-D Rectangular Array in the Presence of Gain and Phase Uncertainty

Afkhaminia

Azghani

2021

Circuits Syst Signal Process

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DOA Estimation of Strictly Noncircular Sources in Wireless Sensor Array Network via Block Sparse Representation

Cited by 6 publications

References 31 publications

Real‐Valued Weighted Subspace Fitting Algorithm for DOA Estimation with Block Sparse Recovery

Real‐Valued Weighted Subspace Fitting Algorithm for DOA Estimation with Block Sparse Recovery

Knowledge‐aided block sparse Bayesian learning STAP for phased‐array MIMO airborne radar

Sparsity-based DOA Estimation of 2-D Rectangular Array in the Presence of Gain and Phase Uncertainty

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