Real‐valued off‐grid DOA estimation based on fourth‐order cumulants using sparse Bayesian learning in spatial coloured noise

Pan, Meihong; Zhang, Gong; Hu, Zhentao; Zheng, Qin

doi:10.1049/iet-com.2018.6244

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…However, with the continuous in-depth research on subspace class algorithms, the limitations of the algorithms have gradually emerged. Most subspace class algorithms assume that the noise is ideal Gaussian white noise with independent homogeneous distribution, while the noise in the real environment is usually colour noise that does not satisfy this distribution [5][6][7][8][9]. All of these situations lead to degradation of the performance of traditional super-resolution subspace class algorithms and are extremely computationally intensive.…”

Section: Introductionmentioning

confidence: 99%

Design of a low-complexity DOA estimation method based on fourth-order cumulants

Lin

2023

International Conference on Optical Technology, Semiconductor Materials, and Devices (OTSMD 2022)

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To address the problem that the conventional algorithm has a very high complexity in estimating the direction of arrival (DOA) of coherent sources in the background of colour noise, this paper proposes a low-complexity DOA estimation method based on the fourth-order cumulant and Toeplitz matrix reconstruction. Firstly, a fourth-order cumulant (FOC) matrix is constructed from the received signal vector to suppress the noise component, and the redundant information in the FOC matrix is removed by storing the matrix to obtain the reduced dimensional FOC matrix. Afterwards, the Toeplitz matrix is reconstructed to extend the array aperture and to achieve decoherence. Finally, the DOA estimation of the reconstructed matrix is performed using the MUSIC algorithm. Simulations demonstrate that the DOA estimation of coherent signals in a colour-noise background is less complex and maintains a higher accuracy than conventional algorithms.

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Section: Introductionmentioning

confidence: 99%

Design of a low-complexity DOA estimation method based on fourth-order cumulants

Lin

2023

International Conference on Optical Technology, Semiconductor Materials, and Devices (OTSMD 2022)

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“…Tufail and Ahmed [15] used the FOC and ESPRIT algorithm to propose DOA estimation based on the genetic algorithm (GA) and obtained the multiple invariant cumulant ESPRITalgorithm, which has a better angular resolution, but the problem of excessive complexity remains unsolved. Literature [16] used the real-valued sparse Bayesian learning method to transform the FOC matrix into a real-valued matrix and simplify the algorithm through unitary transformation. e abovementioned FOC algorithm has made certain progress in the field of array signal processing, but the research on the background of strong interference is scarce.…”

Section: Introductionmentioning

confidence: 99%

DOA Estimation Method of Weak Signal under the Compound Background of Strong Interference and Colored Noise

Lin

Zhou

et al. 2022

International Journal of Antennas and Propagation

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The traditional algorithm performing direction of arrival (DOA) estimation under the background of strong interference and colored noise has the problems of low estimation accuracy and small measurement targets. Based on the construction of a fourth-order cumulant (FOC) matrix to suppress colored noise, this paper adopts the extended noise subspace (ENS) algorithm and the fixed projection blocking (FPB) algorithm to estimate the DOA of weak targets. Firstly, a FOC matrix of the received signal vector is established to curb the noise component, and the eigenvalue decomposition is performed. Then, two approaches of weak signal DOA estimation are proposed. One approach is to merge the space where the strong interference steering vector lies into the noise subspace to construct an extended noise subspace, and then, the multisignal classification (MUSIC) algorithm is used to obtain the DOA estimation of the weak signal on the basis of the extended noise subspace. Another approach is to build the orthogonal projection matrix of the interference subspace as the interference blocking matrix, and the receiving array signal is preprocessed, and on the basis of it, the eigen decomposition is performed again to obtain the DOA information of the weak signal. Both algorithms make breakthroughs in the aperture limitation of the traditional algorithm, effectively expand the aperture, and promote the accuracy of estimation. The simulation tests the effectiveness of the proposed method.

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“…As a result, the cost, volume, weight of the system will be greatly increased, and the payload capability will not be satisfied. Although the high-order cumulant algorithm [31][32][33][34][35] and the nested array [36][37][38][39][40] are used to extend the array aperture, the estimable maximum number of sources are restricted by the number of array elements inevitably, and the computational complexity will be greatly increased as well. Secondly, these algorithms are unable to estimate the very close DOAs for the restriction of the number of array elements.…”

Section: Introductionmentioning

confidence: 99%

DOA estimation algorithm based on DFT and multiple regression

Mao

Zhang

2020

IET Communications

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In order to address the problem that the direction-of-arrival (DOA) estimation is restricted by the number of array elements, this study comes up with a novel DOA estimation algorithm based on discrete-Fourier-transformation (DFT) and multiple regression, which focuses on the direct data domain from each array element rather than the traditional covariance domain of the whole array. In this algorithm, the authors regard each channel of the array signal model as an independent form of multiple regression and achieve the DOA by estimating the regression coefficient of the reconstructed source signal from each array element one by one iteratively. In addition, the DOAs estimated from the multiple array elements are averaged to improve the accuracy by exploiting the asymptotic normality. This algorithm is proposed, modelled, and developed theoretically at first. Then the capabilities that the authors' algorithm can estimate not only the very close DOAs since they distinguish the irrelevant sources by the DFT on temporal frequency but also the DOAs when the number of sources is larger than that of array elements are fully demonstrated by simulations. Furthermore, no requirement for the known number of sources brings the algorithm broader application foreground.

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Real‐valued off‐grid DOA estimation based on fourth‐order cumulants using sparse Bayesian learning in spatial coloured noise

Cited by 4 publications

References 22 publications

Design of a low-complexity DOA estimation method based on fourth-order cumulants

Design of a low-complexity DOA estimation method based on fourth-order cumulants

DOA Estimation Method of Weak Signal under the Compound Background of Strong Interference and Colored Noise

DOA estimation algorithm based on DFT and multiple regression

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