A Noise Suppression Method of Ground Penetrating Radar Based on EEMD and Permutation Entropy

Xue, Wei; Dai, Xiangyang; Zhu, Jichao; Luo, Yao; Yang, Yueting

doi:10.1109/lgrs.2019.2902123

Cited by 26 publications

(18 citation statements)

References 16 publications

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“…Permutation entropy (PE) can detect the randomness and dynamic behavior of a time series. It has a strong anti-interference ability and is suitable for dealing with nonlinear data [23,24]. Given a time series {x(i) i = 1, 2, .…”

Section: Permutation Entropymentioning

confidence: 99%

Optimal Denoising and Feature Extraction Methods Using Modified CEEMD Combined with Duffing System and Their Applications in Fault Line Selection of Non-Solid-Earthed Network

Hou

Guo

2020

Symmetry

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As the non-solid-earthed network fails, the zero-sequence current of each line is highly non-stationary, and the noise component is serious. This paper proposes a fault line selection method based on modified complementary ensemble empirical mode decomposition (MCEEMD) and the Duffing system. Here, based on generalized composite multiscale permutation entropy (GCMPE) and support vector machine (SVM) for signal randomness detection, the complementary ensemble empirical mode decomposition is modified. The MCEEMD algorithm has good adaptability, and it can restrain the modal aliasing of empirical mode decomposition (EMD) at a certain level. The Duffing system is highly sensitive when the frequency of the external force signal is the same as that of the internal force signal. For automatically identifying chaotic characteristics, by using the texture features of the phase diagram, the method can quickly obtain the numerical criterion of the chaotic nature. Firstly, the zero-sequence current is decomposed into a series of intrinsic mode functions (IMF) to complete the first noise-reduction. Then an optimized smooth denoising model is established to select optimal IMF for signal reconstruction, which can complete the second noise-reduction. Finally, the reconstructed signal is put into the Duffing system. The trisection symmetry phase estimation is used to determine the relative phase of the detection signal. The faulty line in the non-solid-earthed network is selected with the diagram outputted by the Duffing system.

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Section: Permutation Entropymentioning

confidence: 99%

Optimal Denoising and Feature Extraction Methods Using Modified CEEMD Combined with Duffing System and Their Applications in Fault Line Selection of Non-Solid-Earthed Network

Hou

Guo

2020

Symmetry

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“…The changes mentioned can be noted in the time domain using several techniques. One regular approach found in the literature is the use of signal decomposition algorithms, such as empirical mode decomposition (EMD) [ 20 ] and its derivatives ensemble and complementary ensemble EMD [ 21 , 22 , 23 ], and autoregressive models [ 24 ]. Some other algorithms are inspired by EMD, such as adaptive local iterative filtering [ 25 ] and fast iterative filtering decomposition [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

Sandoval

Leturiondo

Vidal

et al. 2021

Sensors

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To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration analysis for bearings under such operational conditions. This study proposes the use of indicators based on entropy for monitoring a low-speed bearing condition. The indicators used are approximate, dispersion, singular value decomposition, and spectral entropy of the permutation entropy. This approach has been tested with vibration signals acquired in a test rig with bearings under different health conditions. The results show that entropy indicators (EIs) can discriminate with higher-accuracy damaged bearings for low-speed bearings compared with the regular indicators. Furthermore, it is shown that the combination of regular and entropy-based indicators can also contribute to a more reliable diagnosis.

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“…In the past studies, Shao [8] et al realized the identification of aircraft targets based on waveform features after analyzing the characteristics of low-resolution radar. Up to now, many methods [9][10][11] have been proposed to extract radar echoes from aircraft targets, such as singular value decomposition, periodic diagram, and empirical mode decomposition. However, they are not good at recognizing aircraft targets under short irradiation time and low pulse repetition frequency.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Target Classification Method Based on Eemd and Multifractal

Hu¹,

Li²,

Zhang³

et al. 2021

PIER M

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Due to the limitation of low-resolution radar system and the influence of background clutter in the detection process, it is hard for low-resolution radars to classify and identify aircraft targets. To solve the above problems, a classification method for aircraft based on Ensemble Empirical Mode Decomposition (EEMD) and multifractal is proposed, in which the intrinsic modes are obtained by EEMD, and the waveform entropy in the Doppler domain is used to screen and reconstruct the intrinsic modes. The multifractal feature of the target echo data is extracted from the reconstructed signal, and then the aircraft target classification and recognition experiment is carried out with support vector machine. The experimental results show that the feature data extracted by ensemble empirical mode decomposition and multifractal analysis can be used for the classification and identification of civil aircraft and fighter aircraft, and the accuracy rate is about 98.5%, which is higher than that of timedomain multifractal method.

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A Noise Suppression Method of Ground Penetrating Radar Based on EEMD and Permutation Entropy

Cited by 26 publications

References 16 publications

Optimal Denoising and Feature Extraction Methods Using Modified CEEMD Combined with Duffing System and Their Applications in Fault Line Selection of Non-Solid-Earthed Network

Optimal Denoising and Feature Extraction Methods Using Modified CEEMD Combined with Duffing System and Their Applications in Fault Line Selection of Non-Solid-Earthed Network

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis

Aircraft Target Classification Method Based on Eemd and Multifractal

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