A Comparative Study of Four Kinds of Adaptive Decomposition Algorithms and Their Applications

Liu, Tao; Luo, Zhijun; Huang, Jia-Hong; Yan, Shaoze

doi:10.3390/s18072120

Cited by 75 publications

(44 citation statements)

References 131 publications

(227 reference statements)

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“…Since the signal of the carrier-free UWB radar does not contain carrier frequency information the energy is concentrated in a very narrow waveform, and the correlation between the transmitted signal and the echo signal is weak. Therefore, a human motion echo signal decomposition method based on the 2D-VMD algorithm to extract the characteristic of this type of human motion echo signal is proposed that utilizes several BIMFs components obtained by the 2D-VMD algorithm [30,31,32]; it decomposes the original radar echo signals to reconstruct original echo signal. The 2D-VMD algorithm is a non-recursive variable-scale composition iterative search variational model that decomposes the 2D human motion echo signal of the carrier-free UWB radar into several BIMFs mode components, which represent the primary feature of this type of human motion.…”

Section: Feature Extraction and Reconstruction Model Of Human Motimentioning

confidence: 99%

Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition

Jiang

Zhou

Che

et al. 2019

Sensors

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As the size of the radar hardware platform becomes smaller and smaller, the cost becomes lower and lower. The application of indoor radar-based human motion recognition has become a reality, which can be realized in a low-cost device with simple architecture. Compared with narrow-band radar (such as continuous wave radar, etc.), the human motion echo signal of the carrier-free ultra-wideband (UWB) radar contains more abundant characteristic information of human motion, which is helpful for identifying different types of human motion. In this paper, a novel feature extraction method by two-dimensional variational mode decomposition (2D-VMD) algorithm is proposed. And it is used for extracting the primary features of human motion. The 2D-VMD algorithm is an adaptive non-recursive multiscale decomposition method for nonlinear and nonstationary signals. Firstly, the original 2D radar echo signals are decomposed by the 2D-VMD algorithm to capture several 2D intrinsic mode function (BIMFs) which represent different groups of central frequency components of a certain type of human motion. Secondly, original echo signals are reconstructed according to the several BIMFs, which not only have a certain inhibitory effect on the clutter in the echo signal, but can also further demonstrate that the BIMFs obtained by the 2D-VMD algorithm can represent the original 2D echo signal well. Finally, based on the measured ten different types of UWB radar human motion 2D echo analysis signals, the characteristics of these different types of human motion are extracted and the original echo signal are reconstructed. Then, the three indicators of the PCC, UQI, and PSNR between the original echo signals and extraction/reconstruction 2D signals are analyzed, which illustrate the effectiveness of 2D-VMD algorithm to extract feature of human motion 2D echo signals of the carrier-free UWB radar. Experimental results show that BIMFs by 2D-VMD algorithm can well represent the echo signal characteristics of this type of human motion, which is a very effective tool for human motion radar echo signal feature extraction.

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Section: Feature Extraction and Reconstruction Model Of Human Motimentioning

confidence: 99%

Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition

Jiang

Zhou

Che

et al. 2019

Sensors

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“…The Kalman filter (KF) is an optimal state estimator for linear state-space systems [1][2][3]. It is widely used, owing to its optimality, in many applications like, e.g., localization, control, target tracking, and signal processing [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In reality, however, systems are usually characterized by strong non-linearities which make the conventional KF inappropriate.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise

Dong

Chisci

Cai

2020

Sensors

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Aiming towards state estimation and information fusion for nonlinear systems with heavy-tailed measurement noise, a variational Bayesian Student’s t-based cubature information filter (VBST-CIF) is designed. Furthermore, a multi-sensor variational Bayesian Student’s t-based cubature information feedback fusion (VBST-CIFF) algorithm is also derived. In the proposed VBST-CIF, the spherical-radial cubature (SRC) rule is embedded into the variational Bayes (VB) method for a joint estimation of states and scale matrix, degree-of-freedom (DOF) parameter, as well as an auxiliary parameter in the nonlinear system with heavy-tailed noise. The designed VBST-CIF facilitates multi-sensor fusion, allowing to derive a VBST-CIFF algorithm based on multi-sensor information feedback fusion. The performance of the proposed algorithms is assessed in target tracking scenarios. Simulation results demonstrate that the proposed VBST-CIF/VBST-CIFF outperform the conventional cubature information filter (CIF) and cubature information feedback fusion (CIFF) algorithms.

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“…The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) [22]- [25] is proposed to solve these problems. Compared to the EEMD [26], the CEEMDAN obtains the IMF component by adding adaptive auxiliary noise and calculating the unique margin. As a result, it can maximize the zero reconstruction error of the decomposed signal.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Faults Diagnosis of High-Voltage Circuit Breaker via Hybrid Features and Integrated Extreme Learning Machine

et al. 2019

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As key electrical equipment in the power system, the normal operation of a high-voltage circuit breaker is related to the reliability and economy of the power supply. In this paper, a mechanical fault diagnostic method for a high-voltage circuit breaker via the hybrid feature extraction and the integrated extreme learning machine (IELM) is investigated. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the vibration signal to obtain intrinsic mode functions (IMF). Then, the sub-band reconstruction of each order IMF component is performed by combining the Hilbert transform and the band-pass filter in order to obtain the time-frequency matrix. Moreover, mechanical fault feature vectors can be formed by the time-frequency entropy and the singular entropy, which are extracted by transforming the time-frequency matrix into the energy matrix and normalizing the frequency bands via the normal cumulative distribution function (NCDF). In addition, an IELM is built for the fault classification. The advantages of the proposed CEEMDAN scheme in combination with band-pass filtering can eliminate the modal aliasing, reduce the number of auxiliary noise additions, and improve the decomposition efficiency. Besides, the performance of the singular entropy normalized by the NCDF is more stable, and the IELM composed of multiple weak classifiers can solve the shortcomings of the traditional extreme learning machine. The experimental results based on measured data show the proposed method can effectively diagnose the mechanical failure via small samples of high-voltage circuit breakers. INDEX TERMS High-voltage circuit breaker, vibration signal, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), time-frequency entropy, singular entropy, integrated extreme learning machine (IELM).

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A Comparative Study of Four Kinds of Adaptive Decomposition Algorithms and Their Applications

Cited by 75 publications

References 131 publications

Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition

Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition

An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise

Mechanical Faults Diagnosis of High-Voltage Circuit Breaker via Hybrid Features and Integrated Extreme Learning Machine

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