Direction-of-Departure and Direction-of-Arrival Estimation Algorithm Based on Compressive Sensing: Data Fitting

Paik, Ji Woong; Hong, Wooyoung; Lee, Joon-Ho

doi:10.3390/rs12172773

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…According to formula (6), an equivalent covariance matrix can be obtained. In addition, to further improve performance, FBSS technology was used to further optimize the obtained equivalent R, which is expressed as follows:…”

Section: Equivalent Covariancementioning

confidence: 99%

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Direction-of-Arrival Estimation Method for Principal Singular Vectors Based on Multiple Toeplitz Matrices

Tang

Fan

Lei

et al. 2022

Wireless Communications and Mobile Computing

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A principal singular vector based on multiple Toeplitz matrices is proposed to solve the accuracy problem of direction-of-arrival (DOA) estimation for coherent signals. First, the data matrix received by uniform linear array (ULA) is transformed into a Toeplitz matrix. An equivalent covariance matrix is obtained by square weighted summation method using the Toeplitz matrix. Then, a polynomial containing DOA information is constructed because the signal space and the steering matrix have the same column space; the Toeplitz matrix is built using polynomial coefficients. The problem is transformed into solving linear equations by establishing the relationship between the Toeplitz matrix and the signal subspace. Furthermore, the weighted least square method is used to obtain multiple candidates for linear equations. Finally, the maximum likelihood (ML) rule is used to select source signal candidates from multiple candidates. In comparison with currently known algorithm, the proposed algorithm has the characteristics of high estimation accuracy, low-complexity, and strong anti-interference ability and resolution. Even when the signal-to-noise ratio (SNR) is low, the snapshot number is small, and multiple signals exist; this method can still provide good estimation performance and resolution, which is more than 90% in most cases. Simulation experiments verify the superiority of the algorithm.

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Section: Equivalent Covariancementioning

confidence: 99%

“…Array signal processing is a crucial research issue in signal processing and has been extensively used in radar [1,2], sensor [3,4], remote sensing [5][6][7], target detection [8,9], and wireless communication [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Direction-of-Arrival Estimation Method for Principal Singular Vectors Based on Multiple Toeplitz Matrices

Tang

Fan

Lei

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Considering problems, such as distinguishing coherence sources and separating spatial closely sources, sparse reconstruction theory plays an important role in DOA estimation [3,4]. In dividing the whole spatial domain of interests into a discrete set of potential grids, a traditional estimation module can be converted into an ill-posed inverse problem.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Lp Norm Minimization Algorithm for Direction of Arrival Estimation

Liu

Rao

2022

Remote Sensing

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In this paper, we propose a new direction of arrival (DOA) estimation algorithm, in which DOA estimation is achieved by finding the sparsest support set of multiple measurement vectors (MMV) in an over-complete dictionary. The proposed algorithm is based on ℓp norm minimization, which belongs to non-convex optimization. Therefore, the quasi-Newton method is used to converge the iterative process. There are two advantages of this algorithm: one is the higher possibility and resolution of distinguishing closely spaced sources, and the other is the adaptive regularization parameter adjustment. Moreover, an accelerating strategy is applied in the computation, and a weighted method of the proposed algorithm is also introduced to improve the accuracy. We conducted experiments to validate the effectiveness of the proposed algorithm. The performance was compared with several popular DOA estimation algorithms and the Cramer–Rao bound (CRB).

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“…In order to enhance the DOA estimation performance of the data-fitting algorithm, the single measurement formulation of the conventional algorithm was expanded to the multiple snapshots measurement formulation. In [ 9 ], which is a study on extending the data-fitting DOA algorithm to the bistatic MIMO sonar system is presented, and the high-resolution direction-of-departure (DOD)/DOA algorithm is proposed.…”

Section: Introductionmentioning

confidence: 99%

“…The biggest problem with these algorithms [ 8 , 9 , 10 , 11 , 12 ] is that they are computationally intensive, and they are highly dependent on the noise variance of a given environment. In [ 8 , 9 , 10 , 11 , 12 ], a regularization parameter, which is the weight of the cost function, is highly dependent on the noise variance. If the noise variance that is used for the corresponding parameter does not match the noise variance of the environment, the DOA estimation performance of the corresponding methods is greatly degraded.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix

Paik

Lee

Hong

2021

Sensors

Self Cite

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An enhanced smoothed l0-norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l0-norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data.

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Direction-of-Departure and Direction-of-Arrival Estimation Algorithm Based on Compressive Sensing: Data Fitting

Cited by 10 publications

References 20 publications

Direction-of-Arrival Estimation Method for Principal Singular Vectors Based on Multiple Toeplitz Matrices

Direction-of-Arrival Estimation Method for Principal Singular Vectors Based on Multiple Toeplitz Matrices

An Adaptive Lp Norm Minimization Algorithm for Direction of Arrival Estimation

An Enhanced Smoothed L0-Norm Direction of Arrival Estimation Method Using Covariance Matrix

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