Adaptive Widely Linear Constrained Constant Modulus Reduced-Rank Beamforming

Wu, Xiaomin; Zhao, Minjian; Lamare, Rodrigo C. de; Champagne, Benoı̂t

doi:10.1109/taes.2017.2650838

Cited by 19 publications

(10 citation statements)

References 42 publications

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“…T HERE has been, recently, a great deal of research in the field of sparse reconstruction with Compressive Sensing (CS) algorithms. These techniques have been used for direction-of-arrival (DOA) estimation with several advantages over classical [1] and subspace techniques [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33] as they can cost-effectively perform this task in challenging scenarios with excellent performance [34], [35].…”

Section: Introductionmentioning

confidence: 99%

Study of List-Based OMP and an Enhanced Model for Direction Finding with Non-Uniform Arrays

Leite¹,

Lamare²

2021

Preprint

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This paper proposes an enhanced coarray transformation model (EDCTM) and a mixed greedy maximum likelihood algorithm called List-Based Maximum Likelihood Orthogonal Matching Pursuit (LBML-OMP) for direction-of-arrival estimation with non-uniform linear arrays (NLAs). The proposed EDCTM approach obtains improved estimates when Khatri-Rao product-based models are used to generate difference coarrays under the assumption of uncorrelated sources. In the proposed LBML-OMP technique, for each iteration a set of candidates is generated based on the correlation-maximization between the dictionary and the residue vector. LBML-OMP then chooses the best candidate based on a reduced-complexity asymptotic maximum likelihood decision rule. Simulations show the improved results of EDCTM over existing approaches and that LBML-OMP outperforms existing sparse recovery algorithms as well as Spatial Smoothing Multiple Signal Classification with NLAs.

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Section: Introductionmentioning

confidence: 99%

Study of List-Based OMP and an Enhanced Model for Direction Finding with Non-Uniform Arrays

Leite¹,

Lamare²

2021

Preprint

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“…In order to find an unique mapping between the signal x and the measurement y, the constraint of sparsity on x can be utilized [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], Corresponding author: Sheng Li (email: shengli@zjut.edu.cn). [37], [38], [39], [40], [41], [42], [43], [44], [45]. The sparse representation for x can be expressed as:…”

Section: Introductionmentioning

confidence: 99%

Design of Compressed Sensing System With Probability-Based Prior Information

Jiang

Zhu

et al. 2020

IEEE Trans. Multimedia

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This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing system. With the knowledge of the probability for each atom of the dictionary being used, a diagonal weighted matrix is obtained and then the sensing matrix is designed by minimizing a weighted function such that the Gram of the equivalent dictionary is as close to the Gram of dictionary as possible. An analytical solution for the corresponding sensing matrix is derived which leads to low computational complexity. We also exploit this prior information through the sparse recovery stage and propose a probability-driven orthogonal matching pursuit algorithm that improves the accuracy of the recovery. Simulations for synthetic data and application scenarios of surveillance video are carried out to compare the performance of the proposed methods with some existing algorithms. The results reveal that the proposed CS system outperforms existing CS systems.

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“…However, these beamformers become suboptimal when they encounter noncircular signals. In such cases, the optimal beamforming shall be as widely linear (WL) [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Robust widely linear beamforming using estimation of extended covariance matrix and steering vector

Meng

Zhou

Gazor

2020

J Wireless Com Network

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The distribution of the received signals in many array processing applications is noncircular. Although optimal widely linear beamformer (WLB) can provide the best performance for noncircular received signals, its performance degrades severely under model mismatches in practical applications. As a remedy, we propose a robust WLB by using precise reconstruction of extended interference-plus-noise covariance matrix (EINCM) and low-complexity estimation of extended desired signal steering vector (EDSSV). We propose to first determine the steering vectors, powers, and noncircularity coefficients of all signals and the noise power. In contrast to the previous reconstruction methods using the integration over a wide angular sector, we reconstruct the interference-plus-noise covariance matrix (INCM) and the pseudo INCM accurately according to their definitions. By using INCM and pseudo INCM, we can precisely reconstruct the EINCM. We propose to estimate the EDSSV by intersecting two extended subspaces, which are respectively formed by eigendecomposing the extended sample covariance matrix and the extended desired signal covariance matrix. Unlike the convex optimization methods, the proposed EDSSV estimation does not require any optimization programming and yields a solution with closed expression in low computational complexity. Simulation results show that the proposed robust WLB provides near optimal performance under several model mismatch cases.

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Adaptive Widely Linear Constrained Constant Modulus Reduced-Rank Beamforming

Cited by 19 publications

References 42 publications

Study of List-Based OMP and an Enhanced Model for Direction Finding with Non-Uniform Arrays

Study of List-Based OMP and an Enhanced Model for Direction Finding with Non-Uniform Arrays

Design of Compressed Sensing System With Probability-Based Prior Information

Robust widely linear beamforming using estimation of extended covariance matrix and steering vector

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