Real-Valued Sparse Bayesian Learning for DOA Estimation With Arbitrary Linear Arrays

Dai, Jisheng; So, Hing Cheung

doi:10.1109/tsp.2021.3106741

Cited by 77 publications

(26 citation statements)

References 56 publications

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“…where the definition of Υ is given in (46). Therefore, the Fisher information matrix F can be simplified as…”

Section: Cramér-rao Bound (Crb) For the Doamentioning

confidence: 99%

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Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm

Chen,

Zheng

et al. 2022

Preprint

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The conventional direction of arrival (DOA) estimation methods are performed with multiple receiving channels. In this paper, a changeling DOA estimation problem is addressed in a different scenario with only one full-functional receiving channel. A new unmanned aerial vehicle (UAV) swarm system using multiple lifted reconfigurable intelligent surface (RIS) is proposed for the DOA estimation. The UAV movement degrades the DOA estimation performance significantly, and the existing atomic norm minimization (ANM) methods cannot be used in the scenario with array perturbation. Specifically, considering the position perturbation of UAVs, a new atomic norm-based DOA estimation method is proposed, where an atomic norm is defined with the parameter of the position perturbation. Then, a customized semi-definite programming (SDP) method is derived to solve the atomic norm-based method, where different from the traditional SDP method, an additional transforming matrix is formulated. Moreover, a gradient descent method is applied to refine the estimated DOA and the position perturbation further. Simulation results show that the proposed method achieves much better DOA estimation performance in the RIS-aided UAV swarm system with only one receiving channel than various benchmark schemes.

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“…where the definition of Υ is given in (46). Therefore, the Fisher information matrix F can be simplified as…”

Section: Cramér-rao Bound (Crb) For the Doamentioning

confidence: 99%

“…• SBL method [46,47]: The sparse Bayesian learning (SBL)-based method for the DOA estimation is proposed to obtain a stationary DOA estimation iteratively with the distribution assumption of the received signal.…”

Section: Rmse (Deg)mentioning

confidence: 99%

Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm

Chen,

Zheng

et al. 2022

Preprint

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show abstract

“…To improve the DOA estimation accuracy while keep complexity low, super-resolution methods based on subspace have been proposed, such as the multiple signal classification (MUSIC) algorithm [17], and estimation of signal parameters via rotational invariance techniques (ESPRIT) [18]. With the development of compressed sensing (CS), sparse reconstruction methods have also been proposed for the DOA estimation [19]- [21]. In most CS-based methods, the spatial domain is discretized into grids, which will introduce the off-grid error since the targets cannot at the grids exactly [22,23].…”

Section: Introductionmentioning

confidence: 99%

A RIS-Based Passive DOA Estimation Method for Integrated Sensing and Communication System

Chen¹,

Chen²,

Wang³

2022

Preprint

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Integrated sensing and communication (ISAC) system has received growing attention, especially in the context of B5G/6G development. Combining the reconfigurable intelligent surface (RIS) with wireless communication process, a novel passive sensing technique is formulated in this paper to estimate the direction of arrival (DOA) of the targets, where the control matrix of the RIS is used to to realize the multiple measurements with only one full-functional receiving channel. Unlike the existing methods, the interference signals introduced by wireless communication are also considered. To improve the DOA estimation, a novel atomic norm-based method is proposed to remove the interference signals by the sparse reconstruction. The DOA is estimated after the interference removal by a novel Hankel-based multiple signal classification (MUSIC) method. Then, an optimization method is also developed for the measurement matrix to reduce the power interference signals and keep the measurement matrix's randomness, which guarantees the performance of the sparse reconstruction. Finally, we derive the theoretical Cramér-Rao lower bound (CRLB) for the proposed system on the DOA estimation. Simulation results show that the proposed method outperforms the existing methods in the DOA estimation and shows the corresponding CRLB with different distributions of the sensing node. The code about the proposed method is available online https://github.com/chenpengseu/ PassiveDOA-ISAC-RIS.git.

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“…SR has been successfully used in various fields including signal processing (Dai & So, 2021; Protter et al., 2010), image processing (Gurumoorthy et al., 2010; Suo et al., 2021), remote sensing (Yokoya & Iwasaki, 2015; Zhu et al., 2021), and pattern recognition (Wright et al., 2009; Yang et al., 2021). The core idea of SR lies in the fact that a given testing sample can be approximately expressed by a linear combination of the training samples in an appropriate space.…”

Section: Introductionmentioning

confidence: 99%

Multi‐Manifold Based Sparse Representation With Application to SAR Target Configuration Recognition

Liu

Chen

2022

Radio Science

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A multi‐manifold based sparse representation (MMSR) algorithm which can preserve the local structure of the datasets in the reconstruction space with multiple descriptions is proposed for synthetic aperture radar (SAR) target configuration recognition. Different from the traditional manifold learning‐based algorithms that preserve the local structure of the datasets in the low‐dimensional space, the proposed MMSR guarantees local structure preserving of the datasets after feature extraction and sparse representation. The proposed MMSR can avoid corrupted relationships of the samples to ease the obstacle of target aspect angle sensitivity, which can realize more accurate SAR target configuration recognition. Satisfying results are obtained on the moving and stationary target acquisition and recognition database.

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Real-Valued Sparse Bayesian Learning for DOA Estimation With Arbitrary Linear Arrays

Cited by 77 publications

References 56 publications

Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm

Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm

A RIS-Based Passive DOA Estimation Method for Integrated Sensing and Communication System

Multi‐Manifold Based Sparse Representation With Application to SAR Target Configuration Recognition

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