Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation

Zi, Jiali; Lv, Danju; Liu, Jiang; Xin, Huang; Wang, Yao; Gao, Mingyuan; Xi, Rui; Zhang, Yan

doi:10.3390/s22010118

Cited by 9 publications

(7 citation statements)

References 42 publications

(55 reference statements)

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“…topological estimation [26] Biomedicine CT imaging reconstruction [27] Biological system identification [28] Biosignal separation [29] , biomolecular networks reconstruction [30] Geophysics --Geophysical exploration [31] Military science Autonomous satellite navigation [32] Missile trajectory correction [33] Passive radar [34] Meteorology Numerical weather prediction [35] Meteorological parameter identification [36] -数据推断问题分为状态估计问题、参数辨识问题、盲源分离问题三类. 状态估计问题已知 z 和 h, 需要推断系统状态 x.…”

Section: Roboticsmentioning

confidence: 99%

Data inference: data leakage paradigms and defense methods in cyber-physical systems

Liu¹,

Wang²,

Liu³

et al. 2023

Sci. Sin.-Inf.

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With the high integration of computing units and physical objects, cyber and physical systems are gradually coupled into cyber-physical systems (CPSs). According to the laws of physical systems and operation flow in CPSs, unknown CPS data can be inferred from other known data. The inferred data leakage threat is triggered when an accurate inference path connects between low-and high-security domain data. In this paper, by analyzing CPS data leakage accidents caused by data inference, paradigms of data leakage threats are proposed from two dimensions: data theft and data inference. Data inference is classified into three problem types: state estimation, parameter identification, and blind source separation. The algorithms for data inference are categorized as model-driven, data-driven, and data-model-driven methods. In the case of an electricity market, the process of inferring key parameters of a power system from public electricity price data is demonstrated, verifying that data inference can cause severe CPS data leakage threats. Meanwhile, the challenges of existing data protection methods are investigated. Additionally, the future research of CPS data inference defense and data security governance is discussed.

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Section: Roboticsmentioning

confidence: 99%

Data inference: data leakage paradigms and defense methods in cyber-physical systems

Liu¹,

Wang²,

Liu³

et al. 2023

Sci. Sin.-Inf.

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“…Thus, addressing the challenging task of setting prior knowledge to determine the sparsity of intermediate signals and eliminating noise interference to achieve accurate reconstruction of noisy signals using the OMP algorithm becomes crucial. This paper proposes an improved algorithm, DTM_OMP_ICA, for the orthogonal matching pursuit (OMP) algorithm under a masking strategy [25], based on independent component analysis (ICA) [26,27]. This algorithm enhances the traditional OMP framework.…”

Section: Introductionmentioning

confidence: 99%

An Improved OMP Algorithm for Enhancing the Anti-Interference Performance of Array Antennas

Gao,

Zhang,

et al. 2024

Sensors

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The demand for precise positioning in noisy environments has propelled the development of research on array antenna radar systems. Although the orthogonal matching pursuit (OMP) algorithm demonstrates superior performance in signal reconstruction, its application efficacy in noisy settings faces challenges. Consequently, this paper introduces an innovative OMP algorithm, DTM_OMP_ICA (a dual-threshold mask OMP algorithm based on independent component analysis), which optimizes the OMP signal reconstruction framework by utilizing two different observation bases in conjunction with independent component analysis (ICA). By implementing a mean mask strategy, it effectively denoises signals received by array antennas in noisy environments. Simulation results reveal that compared to traditional OMP algorithms, the DTM_OMP_ICA algorithm shows significant advantages in noise suppression capability and algorithm stability. Under optimal conditions, this algorithm achieves a noise suppression rate of up to 96.8%, with its stability also reaching as high as 99%. Furthermore, DTM_OMP_ICA surpasses traditional denoising algorithms in practical denoising applications, proving its effectiveness in reconstructing array antenna signals in noisy settings. This presents an efficient method for accurately reconstructing array antenna signals against a noisy backdrop.

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“…Additionally, most of these algorithms rely on the Newton optimizer, which is highly sensitive to the initial value and prone to becoming stuck in saddle points. In recent years, swarm intelligence-based BSS objective functions such as kurtosis and negative entropy have gained attention [25][26][27][28][29]. However, these approaches primarily focus on real-valued BSS through instantaneous mixing models, and they rarely consider swarm intelligence as a joint diagonalization optimization algorithm.…”

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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Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation

Cited by 9 publications

References 42 publications

Data inference: data leakage paradigms and defense methods in cyber-physical systems

Data inference: data leakage paradigms and defense methods in cyber-physical systems

An Improved OMP Algorithm for Enhancing the Anti-Interference Performance of Array Antennas

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

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