Application of a 24 GHz FMCW automotive radar for urban target classification

Villeval, Shahar; Bilik, Igal; Gürbüz, Sevgi Zübeyde

doi:10.1109/radar.2014.6875787

Cited by 35 publications

(25 citation statements)

References 13 publications

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“…In earlier works [10][11][12], despite the fact that an FMCW radar was employed, time-varying micro-Doppler signatures were analyzed for human activities and human-vehicle classification. Therefore, in these cases, real-time indications can be challenging because several time measurements are required.…”

Section: Introductionmentioning

confidence: 99%

Doppler-Spectrum Feature-Based Human–Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor

Hyun

Jin

2020

Sensors

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In this paper, we propose a Doppler-spectrum feature-based human–vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering point difference, and magnitude difference rate features based on the characteristics of the Doppler spectrum in two successive frames. We also use an SVM (support vector machine) and BDT (binary decision tree) for training and validation of the three aforementioned features. We measured the signals using a 24-GHz FMCW radar front-end module and a real-time data acquisition module and extracted three features from a walking human and a moving vehicle in the field. We then repeatedly measured the classification decision rate of the proposed algorithm using the SVM and BDT, finding that the average performance exceeded 99% and 96% for the walking human and the moving vehicle, respectively.

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Section: Introductionmentioning

confidence: 99%

Doppler-Spectrum Feature-Based Human–Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor

Hyun

Jin

2020

Sensors

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“…A few studies have been conducted on the distinction between humans and animals. Owing to the different lengths of legs and kinematic signatures of the target species, the gait characteristics extracted from micro-Doppler features have been used to distinguish between humans and animal directly [21,22], and to identify the category of the target by a classifier, such as a K-Nearest Neighbor (KNN) method [23], and deep convolutional neural networks (DCNNs) [24]. However, in most application scenarios of non-contact health monitoring, the targets are stationary, and the strategy based on Micro-Doppler motion characteristics is not feasible.…”

Section: Introductionmentioning

confidence: 99%

Method for Distinguishing Humans and Animals in Vital Signs Monitoring Using IR-UWB Radar

Wang

Zhang

et al. 2019

IJERPH

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Radar has been widely applied in many scenarios as a critical remote sensing tool for non-contact vital sign monitoring, particularly for sleep monitoring and heart rate measurement within the home environment. For non-contact monitoring with radar, interference from house pets is an important issue that has been neglected in the past. Many animals have respiratory frequencies similar to those of humans, and they are easily mistaken for human targets in non-contact monitoring, which would trigger a false alarm because of incorrect physiological parameters from the animal. In this study, humans and common pets in families, such as dogs, cats, and rabbits, were detected using an impulse radio ultrawideband (IR-UWB) radar, and the echo signals were analyzed in the time and frequency domains. Subsequently, based on the distinct in-body structure between humans and animals, we propose a parameter, the respiratory and heartbeat energy ratio (RHER), which reflects the contribution rate of breathing and heartbeat in the detected vital signs. Combining this parameter with the energy index, we developed a novel scheme to distinguish between humans and animals. In the developed scheme, after background noise removal and direct-current component suppression, an energy indicator is used to initially identify the target. The signal is then decomposed using a variational mode decomposition algorithm, and the variational intrinsic mode functions that represent human respiration and heartbeat components are obtained and utilized to calculate the RHER parameter. Finally, the RHER index is applied to rapidly distinguish between humans and animals. Our experimental results demonstrate that the proposed approach more effectively distinguishes between humans and animals in terms of monitoring vital signs than the existing methods. Furthermore, its rapidity and need for only minimal calculation resources enable it to meet the needs of real-time monitoring.

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“…Human identification has various applications, especially in security and surveillance. Hence, it is drawing increasing attention [1][2][3]. In existing works, human identification is usually based on pictures or videos, which are susceptible to light and will invade people's privacy to some extent, limiting their use under many circumstances.…”

Section: Introductionmentioning

confidence: 99%

Heart ID: Human Identification Based on Radar Micro-Doppler Signatures of the Heart Using Deep Learning

Cao

Xia

2019

Remote Sensing

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Human identification based on radar signatures of individual heartbeats is crucial in various applications, including user authentication in mobile devices, identification of escaped criminals, etc. Usually, optical systems employed to recognize humans are sensitive to ambient light environments, while radar does not have such a drawback, since it has high penetration and all-weather capability. Meanwhile, since micro-Doppler characteristics from the heart of different people are distinct and not easy to fake, it can be used for identification. In this paper, we employed a deep convolutional neural network (DCNN) and conventional supervised learning methods to realize heartbeat-based identification. First, the heartbeat signals were acquired by a Doppler radar and processed by short-time Fourier transform. Then, predefined features were extracted for the conventional supervised learning algorithms, while time–frequency graphs were directly inputted to the DCNN since the network had its own feature extraction part. It is shown that the DCNN could achieve average accuracy of 98.5% for identifying four people, and higher than 80% when the number of people was less than ten. For conventional supervised learning algorithms when identifying four people, the accuracy of the support vector machine (SVM) was 88.75%, and the accuracy of SVM–Bayes was 91.25%, while naive Bayes had the lowest accuracy of 80.75%.

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Application of a 24 GHz FMCW automotive radar for urban target classification

Cited by 35 publications

References 13 publications

Doppler-Spectrum Feature-Based Human–Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor

Doppler-Spectrum Feature-Based Human–Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor

Method for Distinguishing Humans and Animals in Vital Signs Monitoring Using IR-UWB Radar

Heart ID: Human Identification Based on Radar Micro-Doppler Signatures of the Heart Using Deep Learning

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