Blind array signal separation and DOA estimation method based on eigenvalue decomposition

Xiong, Kunlai; Yang, Afeng

doi:10.1007/s11760-020-01837-7

Cited by 4 publications

(8 citation statements)

References 7 publications

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“…(2) min( , ) 10) can be further written as 22 IM − (11) Because the CP decomposition of T will result in one (1) V and one (2) V . According to the assumptions, both (1) V and…”

Section: Tensor Constructionmentioning

confidence: 99%

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Signal Slice and Tensor-Based Blind Separation of Underdetermined Mixtures

Luo,

Jin,

Cheng

et al. 2023

Preprint

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A novel underdetermined blind separation method is proposed based on signal slice and tensor decomposition to explore effective statistical information and improve separation performance. Firstly, the whitening signal is partitioned into several slices, and the delay covariance matrix of each slice is calculated. These delay covariance matrices are then stacked into third-order tensors and compressed into low-dimensional core tensors using high-order singular value decomposition. Next, the third-order tensors are decomposed using canonical polyadic decomposition through weight nonlinear least square to obtain the mixed matrix. Finally, by leveraging signal independence, a matrix diagonalization method is employed to recover the source signals. Simulation results demonstrate that the proposed method effectively suppresses the influence of Gaussian noise and improves the estimation accuracy. Moreover, the proposed method achieves superior separation results compared to seven representative approaches.

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“…(2) min( , ) 10) can be further written as 22 IM − (11) Because the CP decomposition of T will result in one (1) V and one (2) V . According to the assumptions, both (1) V and…”

Section: Tensor Constructionmentioning

confidence: 99%

“…Then, the core tensor H can be decomposed by CP using WNLS, and the factor matrix (1) (2) , VV %% and C % is obtained by Eq. ( 30), (1) (2)…”

Section: While K Iter  Domentioning

confidence: 99%

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Signal Slice and Tensor-Based Blind Separation of Underdetermined Mixtures

Luo,

Jin,

Cheng

et al. 2023

Preprint

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“…Blind source separation (BSS) [1,2] aims to recover or reconstruct the source signal using observed data from the receiver channel, even when the source signal and mixing characteristics are unknown. BSS, as a powerful technology in signal processing, has played a crucial role in various domains, including speech separation [3][4][5], wireless communication [6][7][8], anti-main lobe jamming [9][10][11][12][13], and direction of arrival (DOA) estimation [14,15]. BSS has also provided innovative ideas for ongoing research in integrated arrival (DOA) estimation [14,15].…”

Section: Introductionmentioning

confidence: 99%

A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception

Luo,

Jin,

et al. 2023

Electronics

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The separation of time–frequency mixing signals composed of radar, communication, and jamming is the first step in integrated reception processing, which requires higher accuracy for complex blind source separation (CVBSS). However, traditional CVBSS methods have limitations such as low separation accuracy, a slow convergence speed, and poor robustness in low signal-to-noise ratio (SNR) and high jamming-to-signal ratio (JSR) scenarios. To address the above issues, this paper firstly establishes a time delay mixing mathematical model. A robust whitening algorithm is proposed by using the time delay correlation matrix of the observed signal, which is insensitive to noise. Secondly, the joint diagonalized F-parametrization is used as the objective function, and the separation matrix is constructed based on the multiple complex-valued Givens matrices. The complex-valued Givens matrix not only ensures orthogonality in the separation matrix but also effectively reduces the number of parameters to be calculated. This approach guarantees accuracy and simplifies the complexity of the separation process. Finally, the nonlinear chaotic grey wolf optimizer is utilized to search for the optimal rotation angle. The simulation results demonstrate that this algorithm offers higher separation accuracy and requires fewer iterations compared to the traditional algorithm. Additionally, it enhances the accuracy of direction of arrival (DOA) estimation, reduces the communication bit error rate, and enables the joint estimation of the target distance and velocity even in the presence of powerful jamming and a low SNR.

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“…In [17], a new EM-based method for broadband DOA estimation and BSS was proposed, which reduces the complexity of traditional methods. In [18], a method based on eigenvalue decomposition for DOA estimation and blind separation of narrow-band independent signals was presented. The above studies were discussed for ULA geometry and did not involve more complex array structures like planar arrays which are more practical in actual applications.…”

Section: Introductionmentioning

confidence: 99%

Joint Processing of DOA Estimation and Signal Separation for Planar Array Using Fast‐PARAFAC Decomposition

Que

Jin

2021

Wireless Communications and Mobile Computing

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A joint processing of direction of arrival (DOA) and signal separation for planar array is proposed in this paper. Through sensor array processing theory, the output data of a planar array can be reconstructed as a parallel factor (PARAFAC) model, which can be decomposed with the trilinear alternating least square (TALS) algorithm. Aiming at the problem of slow speed on convergence for the standard PARAFAC method, we introduce the propagator method (PM) to accelerate the convergence of the TALS method and propose a novel method to jointly separate signals and estimate the corresponding DOAs. Given the initial angle estimates with PM, the number of iterations of TALS can be reduced considerably. The experiments indicate that our method can carry out signal separation and DOA estimation for typical modulated signals well and remain the same performance as the standard PARAFAC method with lower computational complexity, which verifies that our algorithm is effective.

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Blind array signal separation and DOA estimation method based on eigenvalue decomposition

Cited by 4 publications

References 7 publications

Signal Slice and Tensor-Based Blind Separation of Underdetermined Mixtures

Signal Slice and Tensor-Based Blind Separation of Underdetermined Mixtures

A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception

Joint Processing of DOA Estimation and Signal Separation for Planar Array Using Fast‐PARAFAC Decomposition

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