Micro-Doppler analysis of wheels and pedestrians in ISAR imaging

Ghaleb, Antoine; Vignaud, L.; Nicolas, Jean‐Marie

doi:10.1049/iet-spr:20070113

Cited by 72 publications

(44 citation statements)

References 6 publications

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“…In contrast to human walking analysis in radar applications, a detailed running human model is not available. In the work of Vignaud and Ghaleb, the kinematic behavior of 40 points of a running human are measured [5]. From these measurements the radial velocity as a function of average running velocity and time are modeled.…”

Section: B) Vignaud Modelmentioning

confidence: 99%

“…The main idea is to represent the required posterior density by a set of random samples with associated weights and to compute estimates based on these samples and weights [5]. Suppose a set of particles {x k21…”

Section: A) Dynamic System Analysis With Particle Filtermentioning

confidence: 99%

“…A particle filter in combination with a model-based approach is proposed to analyze and classify human motion (Section II) [3]. The Thalmann model [4] is used for human walking, while the Vignaud model [5] is chosen for human running (Section III). Based on, among others, the movements of some animals, an estimated spectrum for the null-hypothesis was developed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Human motion classification using a particle filter approach: multiple model particle filtering applied to the micro-Doppler spectrum

Groot

Harmanny

Driessen

et al. 2013

Int. J. Microw. Wireless Technol.

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In this article, a novel motion model-based particle filter implementation is proposed to classify human motion and to estimate key state variables, such as motion type, i.e. running or walking, and the subject's height. Micro-Doppler spectrum is used as the observable information. The system and measurement models of human movements are built using three parameters (relative torso velocity, height of the body, and gait phase). The algorithm developed has been verified on simulated and experimental data.

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Section: B) Vignaud Modelmentioning

confidence: 99%

Section: A) Dynamic System Analysis With Particle Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Human motion classification using a particle filter approach: multiple model particle filtering applied to the micro-Doppler spectrum

Groot

Harmanny

Driessen

et al. 2013

Int. J. Microw. Wireless Technol.

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show abstract

“…Specific analysis and experiments about the analysis of wheels and pedestrian in ISAR was presented in [22]. The analysis first focussed on the model of the m-D effect from wheels of civil vehicles, demonstrating (with both simulated and real data) the possibility of extracting all the information required to correctly identify size, position, aspect angle and rotating frequency of a rotating wheel.…”

Section: Inverse Sarmentioning

confidence: 99%

Developments in target micro-Doppler signatures analysis: radar imaging, ultrasound and through-the-wall radar

Clemente

Balleri

Woodbridge

et al. 2013

EURASIP J. Adv. Signal Process.

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Target motions, other than the main bulk translation of the target, induce Doppler modulations around the main Doppler shift that form what is commonly called a target micro-Doppler signature. Radar micro-Doppler signatures are generally both target and action specific and hence can be used to classify and recognise targets as well as to identify possible threats. In recent years, research into the use of micro-Doppler signatures for target classification to address many defence and security challenges has been of increasing interest. In this article, we present a review of the work published in the last 10 years on emerging applications of radar target analysis using micro-Doppler signatures. Specifically we review micro-Doppler target signatures in bistatic SAR and ISAR, through-the-wall radar and ultrasound radar. This article has been compiled to provide radar practitioners with a unique reference source covering the latest developments in micro-Doppler analysis, extraction and mitigation techniques. The article shows that this research area is highly active and fast moving and demonstrates that micro-Doppler techniques can provide important solutions to many radar target classification challenges.

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“…The CVD characterizes the shape, size, and frequency of the TVD components, which in turn relate to moving parts of the target. CVDs have previously been used to extract features, e.g., [2,16]. An interpretation of the CVD is given in [4,5].…”

Section: Cadence-velocity Diagram (Cvd)mentioning

confidence: 99%

Features for micro‐Doppler based activity classification

Björklund

Petersson

Hendeby

2015

IET Radar, Sonar & Navigation

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Safety and security applications benefit from better situational awareness. Radar micro-Doppler signatures from an observed target carry information about the target's activity, and have potential to improve situational awareness. This article describes, compares, and discusses two methods to classify human activity based on radar micro-Doppler data. The first method extracts physically interpretable features from the time-velocity domain such as the main cycle time and properties of the envelope of the micro-Doppler spectra and use these in the classification. The second method derives its features based on the components with the most energy in the cadence-velocity domain (obtained as the Fourier transform of the time-velocity domain). Measurements from a field trial show that the two methods have similar activity classification performance. It is suggested that target base velocity and main limb cadence frequency are indirect features of both methods, and that they do often alone suffice to discriminate between the studied activities. This is corroborated by experiments with a reduced feature set. This opens up for designing new more compact feature sets. Also, weaknesses of the methods and the impact of non-radial motion are discussed.

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Micro-Doppler analysis of wheels and pedestrians in ISAR imaging

Cited by 72 publications

References 6 publications

Human motion classification using a particle filter approach: multiple model particle filtering applied to the micro-Doppler spectrum

Human motion classification using a particle filter approach: multiple model particle filtering applied to the micro-Doppler spectrum

Developments in target micro-Doppler signatures analysis: radar imaging, ultrasound and through-the-wall radar

Features for micro‐Doppler based activity classification

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