A Novel Block Sparse Reconstruction Method for DOA Estimation With Unknown Mutual Coupling

Zhang, Xiaowei; Jiang, Tao; Li, Yingsong; Zakharov, Yuriy

doi:10.1109/lcomm.2019.2929384

Cited by 29 publications

(15 citation statements)

References 17 publications

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“…In many cases, sparse unknown systems have only a few nonzero entries, and these limited nonzero or large coefficients response appear independently in different locations over a long pulse. Different from the general sparse signal, another type of sparse signal is called the block-structured sparse signal, and it has a special cluster structure in the form of non-zero coefficients [7][8][9][10]. Nonzero coefficients can be located randomly in general sparse systems, but for a block sparse system, the impulse response typically consists of one or several clusters in which nonzero coefficients gather.…”

Section: Introductionmentioning

confidence: 99%

Block-Sparsity Log-Sum-Induced Adaptive Filter for Cluster Sparse System Identification

et al. 2020

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In this work, an effective adaptive block-sparsity log-sum least mean square (BSLS-LMS) algorithm is proposed to improve the convergence performance of cluster sparse system identification. The main idea of the proposed scheme is to add a new block-sparsity induced term into the cost function of the LMS algorithm. We utilize the 1 norm of adaptive tap weights and log-sum as a mixed constraint, via optimizing the cost function through the gradient descent method, the proposed adaptive filtering method can iteratively move the identified signals towards the optimal solutions, and finally identify the unknown system accurately. The cluster-sparse system response, with block length and arbitrary average sparsity, is generated by a Markov-Gaussian (M-G) model. For the white Gaussian input data, the theoretical formulas of the steady-state mis-adjustment and convergence behaviors of the BSLS-LMS are derived in a general sparse system and a block sparse system, respectively. Numerical experiments demonstrate that the proposed adaptive BSLS-LMS algorithm achieves much better convergence behavior than conventional sparse signal recovery solutions. The experimental study also verifies the consistency between the simulation results and the theoretical analysis.

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Section: Introductionmentioning

confidence: 99%

Block-Sparsity Log-Sum-Induced Adaptive Filter for Cluster Sparse System Identification

et al. 2020

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“…As is known to us, the l 2 -and l 1 -norm regularizations are convex. On the contrary, the l 0 -norm problem is not convex, and it is NP-hard [21]- [23]. Nowadays, no simple and effective ways to solve NP-hard problem have been developed [20].…”

Section: Introductionmentioning

confidence: 99%

Improved Primal–Dual Interior-Point Method Using the Lawson-Norm for Inverse Problems

Shang

Xue

et al. 2020

IEEE Access

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Electrical impedance tomography (EIT), geophysics and undersea target reconstruction are typical non-linear ill-posed inverse problems, and in many cases, the anomalous bodies feature with a clear boundary. Thus, it is suitable to obtain sharp boundaries and blocky features with the Total Variation (TV) functional regularization. However, the TV function regularization leads to a non-differentiable objective function at zero in the inverse formulation and reduces the algorithm robustness. In this paper, we propose an improved primal-dual interior-point method (PD-IPM) based on the Lawson norm to get sharp spatial profiles of the anomalous bodies. Furthermore, the impact of the smooth parameter is investigated to get the inverse results. Numerical experiment using simulated data is setup to support our claim. INDEX TERMS Inverse problems, improved primal-dual interior-point method, Lawson norm.

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“…A gridless sparse method called gridless sparse iterative covariance-based estimation (GL-SPICE, GLS) can estimate DOA without grid selection, which is very practical to relieve the grid mismatch problem of spare recovery [32,33]. The literature [34] addressed the DOA estimation with a block sparse reconstruction method in the presence of unknown mutual coupling. But GLS [33] and the method presented in [34] can only deal with linear arrays.…”

Section: Introductionmentioning

confidence: 99%

“…The literature [34] addressed the DOA estimation with a block sparse reconstruction method in the presence of unknown mutual coupling. But GLS [33] and the method presented in [34] can only deal with linear arrays.…”

Section: Introductionmentioning

confidence: 99%

Sparsity-Aware Noise Subspace Fitting for DOA Estimation

Zheng

Chen

Wang

2019

Sensors

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We propose a sparsity-aware noise subspace fitting (SANSF) algorithm for direction-of-arrival (DOA) estimation using an array of sensors. The proposed SANSF algorithm is developed from the optimally weighted noise subspace fitting criterion. Our formulation leads to a convex linearly constrained quadratic programming (LCQP) problem that enjoys global convergence without the need of accurate initialization and can be easily solved by existing LCQP solvers. Combining the weighted quadratic objective function, the 1 norm, and the non-negative constraints, the proposed SANSF algorithm can enhance the sparsity of the solution. Numerical results based on simulations, using real measured ultrasonic, and radar data, show that, compared to existing sparsity-aware methods, the proposed SANSF can provide enhanced resolution with a lower computational burden.

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A Novel Block Sparse Reconstruction Method for DOA Estimation With Unknown Mutual Coupling

Cited by 29 publications

References 17 publications

Block-Sparsity Log-Sum-Induced Adaptive Filter for Cluster Sparse System Identification

Block-Sparsity Log-Sum-Induced Adaptive Filter for Cluster Sparse System Identification

Improved Primal–Dual Interior-Point Method Using the Lawson-Norm for Inverse Problems

Sparsity-Aware Noise Subspace Fitting for DOA Estimation

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