Adaptive Complex Variational Mode Decomposition for Micro-Motion Signal Processing Applications

Xia, Saiqiang; Yang, Jun; Cai, Wanyong; Zhang, Chaowei; Hua, Liangfa; Zhou, Zibo

doi:10.3390/s21051637

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…It is necessary to increase the capacity of complex processing. e common methods are to project the complex signals in different directions or separate the positive and negative frequency components [33,34], but these methods have the problems of loss of signal components and a large amount of calculation.…”

Section: Cvmementioning

confidence: 99%

Removal of Micro-Doppler Effects in ISAR Imaging Based on the Joint Processing of Singular Value Decomposition and Complex Variational Mode Extraction

Xu¹,

Yang²,

Lv³

et al. 2022

Mathematical Problems in Engineering

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For inverse synthetic aperture radar (ISAR) imaging of targets with micromotion parts, the removal of micro-Doppler (m-D) effects is the key procedure. However, under the condition of a sparse aperture, the echo pulse is limited or incomplete, giving rise to the difficulty of eliminating m-D effects. Thus, a novel m-D effects removal algorithm is proposed, which can effectively eliminate m-D effects, as well as the interference introduced by noise and sparse aperture in the ISAR image of the main body. The proposed algorithm mainly includes two processing steps. First, combined with the cut-off point determined by the normalized singular value difference spectrum, the rough estimation of the main body is achieved by singular value decomposition (SVD). Then, the variational mode extraction (VME) is extended to complex variational mode extraction (CVME). The constrained variational problem constructed by bandwidth and spectral overlap constraints is solved by the alternating direction method of multipliers (ADMM), and the precise estimation of the main body is obtained. Experimental results based on both simulated and measured data demonstrate that the proposed algorithm can acquire the high-resolution ISAR image of the main body under noisy and sparse conditions.

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Section: Cvmementioning

confidence: 99%

Removal of Micro-Doppler Effects in ISAR Imaging Based on the Joint Processing of Singular Value Decomposition and Complex Variational Mode Extraction

Xu¹,

Yang²,

Lv³

et al. 2022

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…And the ground clutter in the field environment is strong, clutter suppression and translational motion compensation is needed to carry out for the measured data. In this section, VMD [14,23] and principal component analysis (PCA) [24] are selected to complete this work. Fig.…”

Section: Experiments and Performance Comparisonmentioning

confidence: 99%

Feature Extraction Method of Helicopter Target Based on Flicker Phenomenon Combined With Phase Compensation

Xia¹,

Yang²,

Sun³

et al. 2022

IEEE Access

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For the typical multi-component periodic micro-Doppler signal parameter estimation, a helicopter rotor feature extraction method based on flicker phenomenon and phase compensation is proposed in this paper. The method firstly analyzes the formation mechanism of helicopter rotor flicker phenomenon in time domain and time-frequency domain through the integral model. Then the demodulation operator of the micro-Doppler signal is constructed for the phase compensation of the echoes with respect to the time-frequency flicker characteristics and the phase information of the slow time dimension of the target echoes. The parameter combination of the possible number of blades and rotational speed is predicted using the time domain flicker interval, and the phase compensation process is evaluated and optimized to significantly reduce the amount of computation. Finally, the parameter estimation of rotor blade number, blade length and rotational speed is acquired by calculating whether the center frequency of each flicker after phase compensation is periodically focused to zero frequency. The simulation results show that the method can effectively estimate the main parameters of helicopter rotor blades, and the processing results of the measured data show that the proposed method has more stable performance compared with OMP algorithm and Hough transform, which provides a new technical way for helicopter rotor blade feature extraction.

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“…Compared with EMD, the EEMD method and variational mode decomposition (VMD) method constructs the Wiener filter according to the center frequency of the component and takes the narrow-band property of the component into full consideration [ 31 ], so the frequency band of filtering is more concentrated, and the signal can be decomposed into components with coefficient characteristics adaptively [ 32 ]. With a solid theoretical foundation, the VMD method has been successfully applied in many fields such as seismic data analysis [ 33 , 34 , 35 ], Time-Varying system identification [ 36 ], structural health monitoring [ 37 , 38 ], Micro-Motion signal processing [ 39 ], fault detection and classification [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Seismic Signal Analysis Based on Variational Mode Decomposition and Hilbert Transform for Ground Intrusion Activity Classification

Sun

Qian

Zheng

et al. 2023

Sensors

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The identification of ground intrusion is a key and important technology in the national public security field. In this paper, a novel variational mode decomposition (VMD) and Hilbert transform (HT) is proposed for the classification of seismic signals generated by ground intrusion activities using a seismic sensing system. Firstly, the representative seismic data, including bicycles, vehicles, footsteps, excavations, and environmental noises, were collected through the designed experiment. Secondly, each original datum is decomposed through VMD and five Band-limited intrinsic mode functions (BIMF) are obtained, respectively, which will be used to generate a corresponding marginal spectrum that can reflect the actual frequency component of the signal accurately by HT. Then, three features related to the marginal spectrum, including marginal spectrum energy, marginal spectrum entropy, and marginal spectrum dominant frequency, are extracted for the analysis of the multi-classification using the support vector machine (SVM) classifier with the LIBSVM library. For the sake of testing and verifying the effectiveness of the proposed variational mode decomposition and Hilbert transform (VMD-HT) technique, the evaluation indicators including accuracy, precision, recall, and F1-Score are used and the results are compared with the time domain, frequency domain, ensemble empirical mode decomposition (EEMD), and empirical wavelet transform (EWT) combined with the HT analysis method. The performance of the VMD-HT method for ground intrusion activity classification provides an average value of 99.50%, 98.76%, 98.76%, and 98.75% for the four evaluation indicators, which are higher than all the other contrasted methods.

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Adaptive Complex Variational Mode Decomposition for Micro-Motion Signal Processing Applications

Cited by 10 publications

References 27 publications

Removal of Micro-Doppler Effects in ISAR Imaging Based on the Joint Processing of Singular Value Decomposition and Complex Variational Mode Extraction

Removal of Micro-Doppler Effects in ISAR Imaging Based on the Joint Processing of Singular Value Decomposition and Complex Variational Mode Extraction

Feature Extraction Method of Helicopter Target Based on Flicker Phenomenon Combined With Phase Compensation

Seismic Signal Analysis Based on Variational Mode Decomposition and Hilbert Transform for Ground Intrusion Activity Classification

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