Analysis of radar micro-Doppler signatures from experimental helicopter and human data

Thayaparan, T.; Abrol, Sidharth; Riseborough, E.; Stanković, Ljubiša; Lamothe, Dan; Duff, G.

doi:10.1049/iet-rsn:20060103

Cited by 281 publications

(160 citation statements)

References 18 publications

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“…The S-method is proposed in [11] for microDoppler based characterization. Reassigned joint time-frequency transforms are proposed in [12] for analysis.…”

Section: Introductionmentioning

confidence: 99%

Through-the-Wall Detection of Stationary Human Targets Using Doppler Radar

Narayanan¹,

Shastry²,

Chen³

et al. 2010

PIER B

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Abstract-In homeland security and law enforcement situations, it is often required to remotely detect human targets obscured by walls and barriers. In particular, we are specifically interested in scenarios that involve a human whose torso is stationary. We propose a technique to detect and characterize activity associated with a stationary human in through-the-wall scenarios using a Doppler radar system. The presence of stationary humans is identified by detecting Doppler signatures resulting from breathing, and movement of the human arm and wrist. The irregular, transient, non-uniform, and non-stationary nature of human activity presents a number of challenges in extracting and classifying Doppler signatures from the signal. These are addressed using bio-mechanical human arm movement models and the empirical mode decomposition (EMD) algorithm for Doppler feature extraction. Experimental results demonstrate the effectiveness of our approach to extract Doppler signatures corresponding to human activity through walls using a 750-MHz Doppler radar system.

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“…The S-method is proposed in [11] for microDoppler based characterization. Reassigned joint time-frequency transforms are proposed in [12] for analysis.…”

Section: Introductionmentioning

confidence: 99%

Through-the-Wall Detection of Stationary Human Targets Using Doppler Radar

Narayanan¹,

Shastry²,

Chen³

et al. 2010

PIER B

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“…The motion models are constructed using a simplified point-scatterer basis. Point scatterer basis is a simple and approximate way to model a radar target [17,18]. In the particular case of a turbine, each blade of the turbine can be modeled as a set of closely spaced point scatterers.…”

Section: Motion Modelsmentioning

confidence: 99%

Time-Frequency and Isar Characteristics of Wind Turbines With Higher Order Motions

Naqvi¹,

Líu²

2013

PIER

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Abstract-Radar features from higher order motions of a wind turbine undergoing rotation are studied. Mathematical models for the motions are proposed and used to simulate the joint time-frequency (JTF) and inverse synthetic radar aperture (ISAR) characteristics of the motions. The motions are studied for an isolated turbine as well as for a turbine rotating above a ground. Selected motions are corroborated by laboratory model measurements.

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“…More precisely, the micro-Doppler signal is generally non-linear and non-stationary (Smith, 2008), and as a result traditional signal separation methods such as frequency filtering may fail in this case. Therefore, various methods such as the wavelet transform (Thayaparan et al, 2007;Shah et al,2015), chirplet transform (Thayaparan et al, 2005), Empirical Mode Decomposition (EMD) (Li et al, 2011), and discrete fractional Fourier transform (DFrFT) (Chen et al, 2014) have been applied for the separation of micro-Doppler signals. Both chirplet and DFrFT useadopt the fixed base functions requiring, there are a large number of parameters that need to be estimated during the decomposition.…”

Section: Introductionmentioning

confidence: 99%

Ensemble-empirical-mode-decomposition based micro-Doppler signal separation and classification

Chen

Lin

Emrith

et al. 2017

IJCAT

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Abstract:The target echo signals obtained by Synthetic Aperture Radar (SAR) and Ground Moving Target Indicator (GMTI) platforms are mainly composed of two parts, the microDoppler signal and the target body part signal. The wheeled vehicle and the track vehicle are classified according to the different character of their micro-Doppler signal. In order to overcome the mode mixing problem in Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) is employed to decompose the original signal into a number of Intrinsic Mode Functions (IMF). The correlation analysis is then carried out to select IMFs which have a relatively high correlation with the micro-Doppler signal. Thereafter, four discriminative features are extracted and Support Vector Machine (SVM) classifier is applied for classification. The experimental results show that the features extracted after EEMD decomposition are effective, with up 90% success rate for classification using one feature. In addition, these four features are complementary in different target velocity and azimuth angles.Keywords: micro-Doppler; micro-motion; EEMD; IMF; wheeled/tracked vehicle; SAR/GMTI; signal separation; feature abstraction; vehicle classification; SVM Reference to this paper should be made as follows: Chen, H., Lin, P., Emrith,K., Narayan, P. and and classification, statistical learning for vision based systems, dimensionality reduction techniques, and higher-order statistics.

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Analysis of radar micro-Doppler signatures from experimental helicopter and human data

Cited by 281 publications

References 18 publications

Through-the-Wall Detection of Stationary Human Targets Using Doppler Radar

Through-the-Wall Detection of Stationary Human Targets Using Doppler Radar

Time-Frequency and Isar Characteristics of Wind Turbines With Higher Order Motions

Ensemble-empirical-mode-decomposition based micro-Doppler signal separation and classification

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