Micro-Range/Micro-Doppler Decomposition of Human Radar Signatures

Fogle, Orelle R.; Rigling, Brian D.

doi:10.1109/taes.2012.6324677

Cited by 46 publications

(19 citation statements)

References 38 publications

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“…As the pseudo-Zernike polynomials are defined on the unit circle the CVD dimension is scaled, before the coefficient is computed, to avoid information loss. Applying (11) to (v, ε), the pseudo-Zernike expansion is obtained as…”

Section: B Feature Extraction Algorithmmentioning

confidence: 99%

“…In particular, specific components of micro-Doppler gait signature can be related to parts of the body for identification purposes [10]. Furthermore, in [11] a novel technique for human signatures decomposition into the responses of different body parts has been proposed. Despite the quasi-complete knowledge of the phenomenon and its representation [6], an open problem related to the exploitation of micro-Doppler signatures is the realization of a reliable, robust, and efficient procedure to classify targets on different observation conditions.…”

Section: Introductionmentioning

confidence: 99%

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A novel algorithm for radar classification based on doppler characteristics exploiting orthogonal Pseudo-Zernike polynomials

Clemente

Pallotta

Maio

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

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Phase modulation induced by target micromotions introduces sidebands in the radar spectral signature returns. Time-frequency distributions facilitate the representation of such modulations in a micro-Doppler signature that is useful in the characterization and classification of targets. Reliable micro-Doppler signature classification requires the use of robust features that are capable of uniquely describing the micromotion. Moreover, future applications of micro-Doppler classification will require meaningful representation of the observed target by using a limited set of values. In this paper, the application of the pseudo-Zernike moments for micro-Doppler classification is introduced. Specifically, the proposed algorithm consists of the extraction of the pseudo-Zernike moments from the cadence velocity diagram (CVD). The use of pseudo-Zernike moments allows invariant features to be obtainedManuscript that are able to discriminate the content of two-dimensional matrices with a small number of coefficients. The analysis has been conducted both on simulated and on real radar data, demonstrating the effectiveness of the proposed approach for classification purposes.

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Section: B Feature Extraction Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel algorithm for radar classification based on doppler characteristics exploiting orthogonal Pseudo-Zernike polynomials

Clemente

Pallotta

Maio

et al. 2015

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

show abstract

“…Therefore, the dominant frequency during one chirp will represent the range frequency (f r ) and the Doppler frequency (f d ) can be interpreted from multiple ramps. Accordingly, a joint analysis to extend the velocity µ-D signatures with a micro-range (µ-R) dimension can be developed [6], [7]. This technique can then be deployed to construct a three dimensional signature (t − v − r).…”

Section: Introductionmentioning

confidence: 99%

Real-time capable micro-Doppler signature decomposition of walking human limbs

Abdulatif

Aziz

Kleiner

et al. 2017

2017 IEEE Radar Conference (RadarConf)

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Unique micro-Doppler signature (μ-D) of a human body motion can be analyzed as the superposition of different body parts μ-D signatures. Extraction of human limbs μ-D signatures in real-time can be used to detect, classify and track human motion especially for safety application. In this paper, two methods are combined to simulate μ-D signatures of a walking human. Furthermore, a novel limbs μ-D signature time independent decomposition feasibility study is presented based on features as μ-D signatures and range profiles also known as micro-Range (μ-R). Walking human body parts can be divided into four classes (base, arms, legs, feet) and a decision tree classifier is used. Validation is done and the classifier is able to decompose μ-D signatures of limbs from a walking human signature on real-time basis

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“…To resolve these problems, shape/motion estimation techniques using small numbers of radars have recently been studied and developed [5]- [7]. However, their resolution is insufficient and/or excessive computational resources and relatively large calculation times are required for these applications.…”

Section: Introductionmentioning

confidence: 99%

Velocity/Shape Estimation Algorithm Using Tracking Filter and Data Fusion of Dual Doppler Radar Interferometers

Saho¹,

Masugi²

2015

IJCEE

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An algorithm is proposed for estimating the shape and velocity of a moving target using dual Doppler radars. The proposed algorithm estimates a target velocity and orbit with a fusion of parameters measured by the dual Doppler radar interferometers and the α-β-γ tracking filtering. The accurate shape is then estimated by motion compensation using the estimated velocities. Numerical simulations verify that the proposed algorithm achieves accurate and real-time velocity/shape estimation. The root-mean-square error for velocity estimation is 1.20 cm/s and for shape estimation is 0.317 mm, which corresponds to 1/237 of the nominal resolution determined by the radar bandwidth.

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Micro-Range/Micro-Doppler Decomposition of Human Radar Signatures

Cited by 46 publications

References 38 publications

A novel algorithm for radar classification based on doppler characteristics exploiting orthogonal Pseudo-Zernike polynomials

A novel algorithm for radar classification based on doppler characteristics exploiting orthogonal Pseudo-Zernike polynomials

Real-time capable micro-Doppler signature decomposition of walking human limbs

Velocity/Shape Estimation Algorithm Using Tracking Filter and Data Fusion of Dual Doppler Radar Interferometers

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