Analysis of micro-Doppler signatures

Chen, V. C.; Li, F.; Ho, Shen-Shyang; Wechsler, Harry

doi:10.1049/ip-rsn:20030743

Cited by 324 publications

(169 citation statements)

References 5 publications

(5 reference statements)

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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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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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“…Remote sensing for the recognition and classification of various human activities using radar has attracted great attention from researchers [1][2][3][4][5][6][7][8] since Victor Chen introduced micro-motion in radar observation [9][10][11], especially for finer-grained human activities (e.g., waving, jumping, picking up an object, standing with random micro-shaking, etc.). It has a critical and promising applicability in many fields, such as anti-terrorism, post-disaster search and rescue, border control, and patient monitoring in hospitals.…”

Section: Introductionmentioning

confidence: 99%

MHHT-Based Method for Analysis of Micro-Doppler Signatures for Human Finer-Grained Activity Using Through-Wall SFCW Radar

Liang

et al. 2017

Remote Sensing

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Ultra-wideband radar-based penetrating detection and recognition of human activities has become a focus on remote sensing in various military applications in recent years, such as urban warfare, hostage rescue, and earthquake post-disaster rescue. However, an excellent micro-Doppler signature (MDS) extracting method of human motion with high time-frequency resolution, outstanding anti-interference ability, and extensive adaptability, which aims to provide favorable and more detailed features for human activity recognition and classification, especially in the non-free space detection environment, is in great urgency. To cope with the issue, a multiple Hilbert-Huang transform (MHHT) method is proposed for high-resolution time-frequency analysis of finer-grained human activity MDS hidden in ultra-wideband (UWB) radar echoes during the through-wall detection environment. Based on the improved HHT with effective intrinsic mode function (IMF) selection according to the cosine similarity (CS) principle, the improved HHT is applied to each channel signal in the effective channel scope of the UWB radar signal and then integrated along the range direction. The activities of swinging one or two arms while standing at a spot 3 m from a wall were used to validate the abilities of the proposed method for extracting and separating the MDS of different moving body structures with a high time-frequency resolution. Simultaneously, the corresponding relationship between the frequency components in MHHT-based spectra and structures of the moving human body was demonstrated according to the radar Doppler principle combined with the principle of human body kinematics. Moreover, six common finer-grained human activities and a piaffe at different ranges under the through-wall detection environment were exploited to confirm the adaptability of the novel method for different activities and pre-eminent anti-interference ability under a low signal-noise-clutter ratio (SNCR) environment, which is critical for remote sensing in various military application, such as urban warfare, hostage rescue, earthquake post-disaster rescue.

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“…When radar observes a moving target, the constant Doppler shift of the received signal corresponds to the bulk motion of the target, while the micro-Doppler effect represents the motions of the parts of the target relative to its main body [1]. In many applications, the micro-motions can be used as a powerful tool for inferring the status of the target [3]. For example, the micro-Doppler effect caused by swinging of arms of a walking soldier can be used to infer whether he holds a gun [4].…”

Section: Introductionmentioning

confidence: 99%

Gait classification based on micro-Doppler features

Yang

Ritchie

et al. 2016

2016 CIE International Conference on Radar (RADAR)

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This paper focuses on the classification of human gaits based on micro-Doppler signatures. The micro-Doppler signatures can represent detailed information about the human gaits, which helps in judging the threat of a personnel target. The proposed method consists of three major steps. Firstly, the micro-Doppler signatures are obtained by performing time-frequency analysis on the radar data. Then two micro-Doppler features are extracted from the time-frequency domain. Finally, the one-versus-one support vector machine (SVM) is used to realize multi-class classification. Experiments on real data show that, with the selected features, high classification accuracy of the human gaits of interest can be achieved.

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Analysis of micro-Doppler signatures

Cited by 324 publications

References 5 publications

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

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

MHHT-Based Method for Analysis of Micro-Doppler Signatures for Human Finer-Grained Activity Using Through-Wall SFCW Radar

Gait classification based on micro-Doppler features

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