Joint DOA and Polarization Estimation of Conformal Electromagnetic Vector Array

Lan, Xiaoyu; Wang, Jingzhou; Jiang, Lai

doi:10.1109/icet55676.2022.9824042

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Recently, sparse reconstruction methods [ 18 , 19 , 20 , 21 ] have been applied to the theory of DOA estimation and have attracted considerable attention due to their robustness against large background noise. Related methods exploit the sparsity of the incident signals and transform the DOA estimation into a sparse reconstruction problem.…”

Section: Introductionmentioning

confidence: 99%

Robust Tensor-Based DOA and Polarization Estimation in Conformal Polarization Sensitive Array with Bad Data

Lan,

Jiang,

et al. 2024

Sensors

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Partially impaired sensor arrays pose a significant challenge in accurately estimating signal parameters. The occurrence of bad data is highly probable, resulting in random loss of source information and substantial performance degradation in parameter estimation. In this paper, a tensor variational sparse Bayesian learning (TVSBL) method is proposed for the estimate of direction of arrival (DOA) and polarization parameters jointly based on a conformal polarization sensitive array (CPSA), taking into account scenarios with the partially impaired sensor array. First, a sparse tensor-based received data model is developed for CPSAs that incorporates bad data. Then, a column vector detection method is proposed to diagnose the positions of the impaired sensors. In scenarios involving partially impaired sensor arrays, a low-rank matrix completion method is employed to recover the random loss of signal information. Finally, variational sparse Bayesian learning (VSBL) and minimum eigenvector methods are utilized sequentially to obtain the DOA and polarization parameters estimation, successively. Furthermore, the Cramér-Rao bound is given for the proposed method. Simulation results validated the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Robust Tensor-Based DOA and Polarization Estimation in Conformal Polarization Sensitive Array with Bad Data

Lan,

Jiang,

et al. 2024

Sensors

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“…Lan et al EURASIP Journal on Advances in Signal Processing (2022) 2022:93that all the K true sources are incident on the CPSA[28]. As a result, the array steering matrix is where D(θ, ϕ) = G(θ , ϕ) • A(θ, ϕ) and B(θ , ϕ, γ , η) = V (θ , ϕ)Q(γ , η) .…”

mentioning

confidence: 99%

A joint DOA and polarization estimation method based on the conformal polarization sensitive array from the sparse reconstruction perspective

Lan

Jiang

2022

EURASIP J. Adv. Signal Process.

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The conformal polarization sensitive array (CPSA) is formed by placing some vector sensors on the conformal array and it has a wide range of practical application in direction of arrival (DOA) and polarization parameters estimation. However, due to the diversity of each sensor’s direction, the performance of the conventional parameters estimation methods based on the CPSA would decrease greatly, especially under the low signal to noise ratio (SNR) and limited snapshots. In order to solve this problem, a unified framework and sparse reconstruction perspective for joint DOA and polarization estimation based on CPSA is proposed in this paper. Specifically, the array received signal model of the CPSA is formulated first and the two-dimensional spatial sparsity of the incident signals is then exploited. Subsequently, after employing the singular value decomposition method to reduce the dimension of array output matrix, the variational sparse Bayesian learning and orthogonal matching pursuit methods are utilized to solve the source DOA estimation, respectively. Finally, the polarization parameters are obtained by the minimum eigenvector method. Simulation results demonstrate that the novel approaches can provide improved estimation accuracy and resolution with low SNR and limited snapshots.

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Joint DOA and Polarization Estimation of Conformal Electromagnetic Vector Array

Cited by 2 publications

References 5 publications

Robust Tensor-Based DOA and Polarization Estimation in Conformal Polarization Sensitive Array with Bad Data

Robust Tensor-Based DOA and Polarization Estimation in Conformal Polarization Sensitive Array with Bad Data

A joint DOA and polarization estimation method based on the conformal polarization sensitive array from the sparse reconstruction perspective

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