A subspace method for array covariance matrix estimation

Rahmani, Mostafa; Atia, George K.

doi:10.1109/sam.2016.7569686

Cited by 10 publications

(12 citation statements)

References 19 publications

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“…This method starts with finding a specific subspace which is used as a constraint for estimation of the covariance matrix. It is shown that vectorizing the covariance matrix (after removal of the noise variance), results in a vector that lies in a subspace called correlation subspace [45]…”

Section: Covariance Matrix Estimation: the Correlation Subspace Methodsmentioning

confidence: 99%

“…Recently a high-quality covariance matrix estimation approach named correlation subspace is proposed [45]. In this approach, first, a solution is given for finding a subspace that spans the vectorized denoised covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

“…Then by imposing some constraints using the estimated subspace on a least-square fitting problem, a denoised version of the covariance matrix is reached. It has been shown that applying this covariance matrix estimation improves the quality of subspace-based methods like MUSIC and Capon [45]. The correlation subspace method has been implemented in two ways, optimal and suboptimal [45,46].…”

Section: Introductionmentioning

confidence: 99%

“…It has been shown that applying this covariance matrix estimation improves the quality of subspace-based methods like MUSIC and Capon [45]. The correlation subspace method has been implemented in two ways, optimal and suboptimal [45,46]. While the optimal solution is computationally complex due to the need for optimizing a convex problem, the suboptimal one gives satisfactory results which only requires a further matrix multiplication.…”

Section: Introductionmentioning

confidence: 99%

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A Sequential MUSIC algorithm for Scatterers Detection 2 in SAR Tomography Enhanced by a Robust Covariance 3 Estimator

Naghavi,

Fazel,

Beheshti

et al. 2022

Preprint

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Synthetic aperture radar (SAR) tomography (TomoSAR) is an appealing tool for the extraction of height information of urban infrastructures. Due to the widespread applications of the MUSIC algorithm in source localization, it is a suitable solution in TomoSAR when multiple snapshots (looks) are available. While the classical MUSIC algorithm aims to estimate the whole reflectivity profile of scatterers, sequential MUSIC algorithms are suited for the detection of sparse point-like scatterers. In this class of methods, successive cancellation is performed through orthogonal complement projections on the MUSIC power spectrum. In this work, a new sequential MUSIC algorithm named recursive covariance canceled MUSIC (RCC-MUSIC), is proposed. This method brings higher accuracy in comparison with the previous sequential methods at the cost of a negligible increase in computational cost. Furthermore, to improve the performance of RCC-MUSIC, it is combined with the recent method of covariance matrix estimation called correlation subspace. Utilizing the correlation subspace method results in a denoised covariance matrix which in turn, increases the accuracy of subspace-based methods. Several numerical examples are presented to compare the performance of the proposed method with the relevant state-of-the-art methods. As a subspace method, simulation results demonstrate the efficiency of the proposed method in terms of estimation accuracy and computational load.

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Section: Covariance Matrix Estimation: the Correlation Subspace Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Sequential MUSIC algorithm for Scatterers Detection 2 in SAR Tomography Enhanced by a Robust Covariance 3 Estimator

Naghavi,

Fazel,

Beheshti

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Inspired by the compressive covariance sensing [ 33 ] that the array covariance matrix has a sparse representation in a dictionary constructed from the steering vectors, and the method for constructing orthogonal constraints by utilizing the spatial feature [ 34 , 35 , 36 ], we consider constructing a subspace in which the correlation matrix lies so that an orthogonal constraint can be constructed for the correlation matrix. Firstly, we give the definitions of the spatial spectrum sampling operations.…”

Section: (Conjugate) Correlation Subspaces and The Proposed Coarramentioning

confidence: 99%

Improved Coarray Interpolation Algorithms with Additional Orthogonal Constraint for Cyclostationary Signals

Song

Shen

2018

Sensors

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Many modulated signals exhibit a cyclostationarity property, which can be exploited in direction-of-arrival (DOA) estimation to effectively eliminate interference and noise. In this paper, our aim is to integrate the cyclostationarity with the spatial domain and enable the algorithm to estimate more sources than sensors. However, DOA estimation with a sparse array is performed in the coarray domain and the holes within the coarray limit the usage of the complete coarray information. In order to use the complete coarray information to increase the degrees-of-freedom (DOFs), sparsity-aware-based methods and the difference coarray interpolation methods have been proposed. In this paper, the coarray interpolation technique is further explored with cyclostationary signals. Besides the difference coarray model and its corresponding Toeplitz completion formulation, we build up a sum coarray model and formulate a Hankel completion problem. In order to further improve the performance of the structured matrix completion, we define the spatial spectrum sampling operations and the derivative (conjugate) correlation subspaces, which can be exploited to construct orthogonal constraints for the autocorrelation vectors in the coarray interpolation problem. Prior knowledge of the source interval can also be incorporated into the problem. Simulation results demonstrate that the additional constraints contribute to a remarkable performance improvement.

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A sequential MUSIC algorithm for scatterers detection in SAR tomography enhanced by a robust covariance estimator

Naghavi

Fazel

Beheshti

et al. 2022

Digital Signal Processing

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A subspace method for array covariance matrix estimation

Cited by 10 publications

References 19 publications

A Sequential MUSIC algorithm for Scatterers Detection 2 in SAR Tomography Enhanced by a Robust Covariance 3 Estimator

A Sequential MUSIC algorithm for Scatterers Detection 2 in SAR Tomography Enhanced by a Robust Covariance 3 Estimator

Improved Coarray Interpolation Algorithms with Additional Orthogonal Constraint for Cyclostationary Signals

A sequential MUSIC algorithm for scatterers detection in SAR tomography enhanced by a robust covariance estimator

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