Human walking estimation with radar

Dorp, P. van; Groen, F.C.A.

doi:10.1049/ip-rsn:20030568

Cited by 222 publications

(113 citation statements)

References 4 publications

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“…However such data is limited to specific parameters from the captured scene such as the particular direction, speed, and human characteristics. In order to study different human motion effects, we opt to use a standard analytical model extracted from empirical data, such as the well-known Boulic model [25][26][27][28].…”

Section: Human Walking Modelmentioning

confidence: 99%

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Analysis of Moving Human Micro-Doppler Signature in Forest Environments

Garcia-Rubia¹,

Kıłıc²,

Dang³

et al. 2014

PIER

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Abstract-Automatic detection of human motion is important for security and surveillance applications. Compared to other sensors, radar sensors present advantages for human motion detection and identification because of their all-weather and day-and-night capabilities, as well as the fact that they detect targets at a long range. This is particularly advantageous in the case of remote and highly cluttered radar scenes. The objective of this paper is to investigate human motion in highly cluttered forest medium to observe the characteristics of the received Doppler signature from the scene. For this purpose we attempt to develop an accurate model accounting for the key contributions to the Doppler signature for the human motion in a forest environment. Analytical techniques are combined with full wave numerical methods such as Method of Moments (MoM) enhanced with Fast Multipole Method (FMM) to achieve a realistic representation of the signature from the scene. Mutual interactions between the forest and the human as well as the attenuation due to the vegetation are accounted for. Due to the large problem size, parallel programming techniques that utilize a Graphics Processing Unit (GPU) based cluster are used.

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Section: Human Walking Modelmentioning

confidence: 99%

“…In [28,33], a simple primitive-based prediction technique to model human gait was proposed. These, however, do not incorporate the mutual coupling effects between different human body parts.…”

Section: Human To Radar Scattering E H-rmentioning

confidence: 99%

Analysis of Moving Human Micro-Doppler Signature in Forest Environments

Garcia-Rubia¹,

Kıłıc²,

Dang³

et al. 2014

PIER

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“…Two different human motion models have commonly been used in the literature: mathematical parametric model [10] and empirical non-parametric model [2]. Although parametric models are designed for some specific motions (i.e.…”

Section: A) Human Target Motion Modelmentioning

confidence: 99%

Self-similarity matrix based slow-time feature extraction for human target in high-resolution radar

Aubry

Chevalier

et al. 2014

Int. J. Microw. Wireless Technol.

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Self-similarity matrix based slow-time feature extraction for human target in high-resolution radar yuan he, pascal aubry, francois le chevalier and alexander yarovoy A new approach is proposed to extract the slow-time feature of human motion in high-resolution radars. The approach is based on the self-similarity matrix (SSM) of the radar signals. The Mutual Information is used as a measure of similarity. The SSMs of different radar signals (high-resolution range profile, micro-Doppler, and range-Doppler video sequence) are compared, and the angel-invariant property of the SSMs is demonstrated. The SSM for different activities (i.e. walking and running) is extracted from range-Doppler video sequence and analyzed. Finally, simulation result is validated by experimental data.Keywords: Self-similarity matrix, Slow-time feature, Human target analysis, High-resolution radar . As a tool to illustrate Doppler spectra along the slow-time axis, the microDoppler images have been studied widely due to their potential in human classification [5]. The range-Doppler (RD) images were also used to analyze distributed human scatterers [2]. Although HHRP, micro-Doppler, and RD images all show certain target information, they are restricted to observe targets in either range, Doppler or RD, and the target slowtime evolution has not been addressed in all these signals.Slow-time behavior for human motions (e.g. walking, crawling, and running) is unique. For example, the standard walking procedure can normally be seen as a periodic movement with a certain cadence frequency. Human periodic motion has been recently analyzed in camera-based systems [6,7]. Cutler and Davis [6] proposed using self-similarity matrix (SSM) of one optical image sequence to detect and analyze periodic motions. For action recognition, Junejo et al. [8] claimed that an important structural stability of SSM can be found for a moving person observed by different cameras. Although radar signals differ significantly from optical images, in this paper we will demonstrate that the SSM theory can be extended for radar signals with some adequate modifications.Our approach is to apply SSM to extract the slow-time feature of typical human radar backscattering (i.e. HRRP, microDoppler image, and RD video sequence). Range-Doppler video sequence (RDVS) [9] is one sequence of RD images, as a function of slow-time. RDVS not only preserves the target range information, but also keeps the Doppler information, as a function of slow-time. The SSMs obtained from different signals will be analyzed, and their angle-invariant characteristic will be demonstrated by comparing the SSMs from radars deployed at different locations. Fourier transform-based periodicity detection method will be developed to extract the cadence frequency of the human gait from SSMs. Finally, the proposed approach will be validated on experimental data. This paper is organized as follows. The radar backscattering from the walking human model is described in Section II. Section III discusses SSM of human backsc...

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“…The point spread function is non-negative symmetric in range and speed and its absolute values are normalized to one: P SF (r, v) = 1. The deconvolution is applied to the absolute value of the range-speed response R v (r, v, t): (4) where N (r, v) is additive noise. The Lucy-Richardson deconvolution method [11] is applied to solve the inverse problem:…”

Section: B Range-speed Deconvolutionmentioning

confidence: 99%

“…Model-based approaches with the Boulic model for estimating human motion parameters are described in [4], [5] and [6].…”

Section: Introductionmentioning

confidence: 99%