Application of empirical mode decomposition in removing fidgeting interference in doppler radar life signs monitoring devices

Mostafanezhad, Isar; Borić–Lubecke, Olga; Lubecke, Victor M.; Mandic, Danilo P.

doi:10.1109/iembs.2009.5333206

Cited by 49 publications

(23 citation statements)

References 5 publications

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“…Although there can be several ways for extraction, a data-driven method needs to be exploited to deal with various cases of noise corruption. In this respect, EMD is an excellent data-driven method since it operates as an adaptive time-varying filtering algorithm based on the intrinsic characteristic of the given signature [28][29][30]. Therefore, in this paper, the first chopping harmonic component is chiefly focused and EMD is considered as an extraction method.…”

Section: Proposed Reconstruction Methods and Its Application Examplementioning

confidence: 99%

Effective Reconstruction of the Rotation-Induced Micro-Doppler From a Noise-Corrupted Signature

Park¹,

Myung²

2013

PIER

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Abstract-This paper presents an effective method for reconstructing the rotation-induced micro-Doppler from a signature corrupted by noise. Based on empirical mode decomposition (EMD), a low-pass filter is employed as its preprocessor in order to effectively extract the first chopping harmonic component of the rotation-induced microDoppler. Then the extracted component is used for reconstructing the original micro-Doppler signature in the joint time-frequency domain. Although it is difficult to interpret the time-frequency representation of the noise-corrupted signature, the reconstruction of the microDoppler enables the acquisition of related information and can be used for complementing other traditional analysis methods. By validating the applicability of the proposed method with measured jet engine modulation (JEM) signatures, we demonstrate that the reconstruction process presented in this paper is expected to be significantly helpful for radar target recongnition in real environments.

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Section: Proposed Reconstruction Methods and Its Application Examplementioning

confidence: 99%

Effective Reconstruction of the Rotation-Induced Micro-Doppler From a Noise-Corrupted Signature

Park¹,

Myung²

2013

PIER

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“…In many situations, this many numbers of modes are irrelevant. Even though, this method doesn't have any strong mathematical background, it is widely used in different kinds of applications like in biomedical signal processing 10 , signal processing and communication 11 . The purpose of the decomposition is to extract the principal modes of signal given by the equation,…”

Section: Empirical Wavelet Transformmentioning

confidence: 99%

Performance Comparison of Variational Mode Decomposition over Empirical Wavelet Transform for the Classification of Power Quality Disturbances Using Support Vector Machine

Aneesh

Kumar

Hisham

2015

Procedia Computer Science

117

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This work considers the classification of power quality disturbances based on VMD (Variational Mode Decomposition) and EWT (Empirical Wavelet Transform) using SVM (Support Vector Machine). Performance comparison of VMD over EWT is done for producing feature vectors that can extract salient and unique nature of these disturbances. In this paper, these two adaptive signal processing methods are used to produce three Intrinsic Mode Function (IMF) components of power quality signals. Feature vectors produced by finding sines and cosines of statistical parameter vector of three different IMF candidates are used for training SVM. Validation for six different classes of power qualities including normal sinusoidal signal, sag, swell, harmonics, sag with harmonics, swell with harmonics is performed using synthetic data in MATLAB. Classification results using SVM shows that VMD outperforms over EWT for feature extraction process and the classification accuracy is tabled.

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“…Until now, EMD-based analysis of radar data has been achieved using the standard single-channel algorithm [5] [6]. Despite the potential of the EMD concept for nonlinear and nonstationary data, in practice there are drawbacks in the algorithm operation.…”

Section: Introductionmentioning

confidence: 99%

Noise assisted multivariate empirical mode decomposition applied to Doppler radar data

Ahrabian

Looney

Tobar

et al. 2012

Sensor Signal Processing for Defence (SSPD 2012)

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The operation of the noise-assisted multivariate empirical mode decomposition (NA-MEMD) algorithm, which represents a breakthrough in data-adaptive analysis, is illustrated for the time-frequency analysis of Doppler radar signals. The performance of the NA-MEMD is here compared to the continuous wavelet transform, for both synthetic and real-world data applications, showing the advantage of the noise-assisted concept in terms of sparse time-frequency localization. For the considered application, we show how the approach gives a clear and natural interpretation of Doppler radar phenomena and enables the accurate tracking of object speeds.

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Application of empirical mode decomposition in removing fidgeting interference in doppler radar life signs monitoring devices

Cited by 49 publications

References 5 publications

Effective Reconstruction of the Rotation-Induced Micro-Doppler From a Noise-Corrupted Signature

Effective Reconstruction of the Rotation-Induced Micro-Doppler From a Noise-Corrupted Signature

Performance Comparison of Variational Mode Decomposition over Empirical Wavelet Transform for the Classification of Power Quality Disturbances Using Support Vector Machine

Noise assisted multivariate empirical mode decomposition applied to Doppler radar data

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