Improved DOA estimation based on real-valued array covariance using sparse Bayesian learning

Wang, Yi; Yang, Minglei; Chen, Baixiao; Xiang, Zhe

doi:10.1016/j.sigpro.2016.06.002

Cited by 8 publications

(6 citation statements)

References 23 publications

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“…The estimation error of the virtual array covariance vector and real-valued covariance vector are defined as ξ ξ ξ = vec(R v − R v ) and ξ ξ ξ T = vec(Ĉ v − C v ), respectively. According to [26], ξ ξ ξ obeys complex Gaussian distribution ξ ξ ξ ∼ CN (0, W), and ξ ξ ξ T obeys Gaussian distribution ξ ξ ξ T ∼ N (0, C), where…”

Section: Unitary Matrix Completionmentioning

confidence: 99%

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Unitary Matrix Completion-Based DOA Estimation of Noncircular Signals in Nonuniform Noise

et al. 2019

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In this paper, a novel direction-of-arrival (DOA) estimation algorithm is proposed for noncircular signals with nonuniform noise by using the unitary matrix completion (UMC) technique. First, the proposed method utilizes the noncircular property of signals to design a virtual array for approximately doubling the array aperture. Then, the virtual complex-valued covariance matrix with the unknown nonuniform noise is transformed into the real-valued one by utilizing the unitary transformation to improve the computational efficiency. Next, a novel UMC method is formulated for the DOA estimation to remove the influence of nonuniform noise. Finally, the DOA without the influence of the unknown noncircularity phase is obtained by using the modified estimation of signal parameters via rotational invariance technique (ESPRIT). Especially, for handling the coherent sources, the forward-backward spatial smoothing technique is utilized to reconstruct a full-rank covariance matrix so that the signal subspace and the noise subspace can be correctly separated. Due to utilizing the extended array aperture and the unitary transformation, the proposed method can identify more sources than the number of physical sensors and provides higher angular resolution and better estimation performance. Compared with the existing DOA estimation algorithms for noncircular signals, the proposed one can effectively suppress the influence of the nonuniform noise. The simulation results are provided to verify the effectiveness and superiority of the proposed method.INDEX TERMS Direction-of-arrival estimation, noncircular, nonuniform noise, unitary matrix completion.

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Section: Unitary Matrix Completionmentioning

confidence: 99%

“…For coherent sources, the procedure of the proposed method is summarized in Algorithm 2. 29), ( 30) and (26).…”

Section: F Summary and Cramer-rao Boundmentioning

confidence: 99%

Unitary Matrix Completion-Based DOA Estimation of Noncircular Signals in Nonuniform Noise

et al. 2019

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“…On the DOA estimation side, researchers show great interest in calculating the most accurate value of the DOA of the signal. The researchers have presented many alternatives to the problems to reach real values for both narrowband and wideband signals . One of the problems is considered to be the low signal‐to‐noise ratio (SNR) value of the signal .…”

Section: Introductionmentioning

confidence: 99%

A probabilistic‐based approach for direction‐of‐arrival estimation and localization of multiple sources

Abdelbari¹,

Bilgehan²

2020

Int J Communication

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Summary This article contributes to science at two points. The first contribution is at a point of introducing a novel direction‐of‐arrival (DOA) estimation method which based on subspaces methods called Probabilistic Estimation of Several Signals (PRESS). The PRESS method provides higher resolution and DOA accuracy than current models. Second contribution of the article is at a point of localizing the unknown signal source. The process of localization achieved by using DOA information for the first time. The importance of localization exists in a large area of engineering applications. The aim is to determine the location of multiple sources by using PRESS with minimum effort of computation. We used the maximum probabilistic process in this study. Initially, all the signals are collected by the array of sensors and accurately identified using the proposed algorithm. The receiver at the best in test estimates the source location using only the knowledge of the geographical latitude and longitude values of the array of sensors. Several test points with an accurately calculated angle of arrival enable us to draw linear lines towards the transmitter. The transmitter location can be accurately identified with the line of interceptions. Simulation and numerical results show the outstanding performance of both the DOA estimation method and transmitter localization approach compared with many classical and new DOA estimation methods. The PRESS localization method first tested at 19°, 26°, and 35° with an signal‐to‐noise ratio (SNR) value of ‐5 dB. The PRESS method produced results with an extremely low bias of 0 and 0.00080°. The simulation tests are repeated and produced results with zero bias, which give the exact location of the unknown source.

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“…In recent years, lots of methods [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ] have been proposed for solving the off-grid problems. Zhang et al [ 24 ] presented a block-sparse Bayesian algorithm to solve the grid mismatch problem, in which the noise variance can be normalized to 1 and thus its effect on the estimation performance can be reduced.…”

Section: Introductionmentioning

confidence: 99%

“…Wu et al [ 31 ] proposed two iterative methods, both of which update the signal power vector and off-grid biases alternately. Wang et al [ 32 ] proposed a real-valued formulation of covariance vector-based relevance vector machine (CVRVM) technique, which is implemented in a real domain and has low computation complexity. However, these methods are all applied on the traditional uniform linear array and they do not utilize the increased DOFs provided by the difference coarray of coprime arrays.…”

Section: Introductionmentioning

confidence: 99%

A Sparse-Based Off-Grid DOA Estimation Method for Coprime Arrays

Zeng

Hou

et al. 2018

Sensors

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Recently, many sparse-based direction-of-arrival (DOA) estimation methods for coprime arrays have become popular for their excellent detection performance. However, these methods often suffer from grid mismatch problem due to the discretization of the potential angle space, which will cause DOA estimation performance degradation when the target is off-grid. To this end, we proposed a sparse-based off-grid DOA estimation method for coprime arrays in this paper, which includes two parts: coarse estimation process and fine estimation process. In the coarse estimation process, the grid points closest to the true DOAs, named coarse DOAs, are derived by solving an optimization problem, which is constructed according to the statistical property of the vectorized covariance matrix estimation error. Meanwhile, we eliminate the unknown noise variance effectively through a linear transformation. Due to finite snapshots effect, some undesirable correlation terms between signal and noise vectors exist in the sample covariance matrix. In the fine estimation process, we therefore remove the undesirable correlation terms from the sample covariance matrix first, and then utilize a two-step iterative method to update the grid biases. Combining the coarse DOAs with the grid biases, the final DOAs can be obtained. In the end, simulation results verify the effectiveness of the proposed method.

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Improved DOA estimation based on real-valued array covariance using sparse Bayesian learning

Cited by 8 publications

References 23 publications

Unitary Matrix Completion-Based DOA Estimation of Noncircular Signals in Nonuniform Noise

Unitary Matrix Completion-Based DOA Estimation of Noncircular Signals in Nonuniform Noise

A probabilistic‐based approach for direction‐of‐arrival estimation and localization of multiple sources

A Sparse-Based Off-Grid DOA Estimation Method for Coprime Arrays

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