A new height-estimation method using FMCW radar Doppler beam sharpening

Laribi, Amir; Hahn, Markus; Dickmann, Jürgen; Waldschmidt, Christian

doi:10.23919/eusipco.2017.8081546

Cited by 26 publications

(9 citation statements)

References 8 publications

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“…the radar device. In principle, the radial velocity should be able to be computed by measuring the delay change over time [9,10].…”

Section: Preliminarymentioning

confidence: 99%

“…It is found that the range and radial velocity of an object corresponds to the vertical and horizontal axis of the spectrum obtained by performing 2D FFT on a radar signal segment [9,10], which is usually referred to as the range-doppler spectrum. As shown for the ideal case in Fig.…”

Section: Preliminarymentioning

confidence: 99%

“…In this work, we attempt to fully exploit the FWMC radar signals to detect the presence and estimate the 3D positions of objects based on DNNs. It is known that for an FWMC radar with multiple antenna receivers, 3D information (i.e., range, elevation and azimuth) of interested objects is embedded in the received radar signals [9,10]. Our motivation for exploiting the DNN-based approach is that radar signals can be preprocessed and treated as images.…”

Section: Introductionmentioning

confidence: 99%

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Object Detection and 3d Estimation Via an FMCW Radar Using a Fully Convolutional Network

Zhang

Wenger

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper considers object detection and 3D estimation using an FMCW radar. The state-of-the-art deep learning framework is employed instead of using traditional signal processing. In preparing the radar training data, the ground truth of an object orientation in 3D space is provided by conducting image analysis, of which the images are obtained through a coupled camera to the radar device. To ensure successful training of a fully convolutional network (FCN), we propose a normalization method, which is found to be essential to be applied to the radar signal before feeding into the neural network. The system after proper training is able to first detect the presence of an object in an environment. If it does, the system then further produces an estimation of its 3D position. Experimental results show that the proposed system can be successfully trained and employed for detecting a car and further estimating its 3D position in a noisy environment.

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“…the radar device. In principle, the radial velocity should be able to be computed by measuring the delay change over time [9,10].…”

Section: Preliminarymentioning

confidence: 99%

Section: Preliminarymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Object Detection and 3d Estimation Via an FMCW Radar Using a Fully Convolutional Network

Zhang

Wenger

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…There are continuous advances made in this area of application, involving further improvement and adaptation of the DBS technique and applying super resolution methods for even greater refinement and image resolution [18][19][20]. The application of DBS for automotive purposes is less reported, there has, however, been research in the use of DBS for automotive height finding using 77 GHz frequency-modulated continuous-wave (FMCW) sensors [21]. This also introduces the use of the high-resolution spectral estimation technique called RELAX [22] to improve DBS resolution.…”

Section: Introductionmentioning

confidence: 99%

Application of Doppler beam sharpening for azimuth refinement in prospective low‐THz automotive radars

Daniel

Stove

Hoare

et al. 2018

IET Radar, Sonar & Navigation

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In this study, the authors investigate the application of the Doppler beam sharpening (DBS) technique for angular refinement to the emerging area of low-terahertz (THz) radar sensing. Ultimately this is to improve radar image quality in the azimuth plane to complement the excellent range resolution and thus improve object classification in low-THz radar imaging systems for autonomous platforms. The study explains the fundamental theory behind the process of DBS and describes the applicability of DBS to automotive sensing, indicating the potential for synthetic beamwidths of a fraction of a degree. Low-THz DBS was experimentally tested under controlled laboratory conditions, not only to accurately localised target objects in Cartesian space but also to provide unique object imaging at low-THz frequencies with wide azimuthal beamwidth antennas. It was shown that a stationary (i.e. non-scanned) wide beam antenna mounted on a moving platform can deliver imagery at least comparable to that produced by physical beamforming, be that steering arrays or narrow beam scanning antennas as in the experimental case presented.

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“…The first method uses a multi-path approach to exploit the height information by finding the difference in time delay between the line-of-sight (LoS) component of the signal, and the non-lineof-sight NLoS component [10]. The second method makes use of the Doppler signature of targets and is known as Doppler beam sharpening (DBS) [11], [12]. If the radar platform is moving, and the movement of the platform is known with a high enough precision, then the Doppler information for a target, combined with the azimuthal angular information, can be used to deduce the targets height from the ground.…”

mentioning

confidence: 99%