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

Hyun, Eugin; Jin, Young-Seok

doi:10.3390/s20072001

Cited by 23 publications

(16 citation statements)

References 24 publications

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“…Hyun, E. et al in their work [6] introduced 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 for frequencymodulated continuous wave (FMCW) radar sensor. Using 24 GHz FMCW radar front-end module and a real-time data acquisition module, thanks to the presented solution, the authors reached the average performance exceeded 99% and 96% for the walking human and the moving vehicle, respectively.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Sensors and System for Vehicle Navigation

Stateczny

Kazimierski

Burdziakowski

2022

Sensors

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Section: Overview Of Contributionsmentioning

confidence: 99%

Sensors and System for Vehicle Navigation

Stateczny

Kazimierski

Burdziakowski

2022

Sensors

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“…The recent 77-79 GHz band fast chirp frequency modulated continuous wave (FMCW) radar can ensure a high range and velocity resolution to classify the target by itself, depending on the waveform design. Research on classifying targets using radar sensor data usually focusses on the radar cross-section (RCS) characteristic of targets [7][8][9][10], the characteristic of the measured signal according to the size and shape of targets [11][12][13] or measured signal patterns over time [14][15][16][17][18]. Because it is difficult to classify targets consistently with an individual characteristic, machine learning techniques are used to classify targets by combining various characteristics [11,15,16,[19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Radar target classification considering unknown classes using deep convolutional neural network ensemble

Lee

Kang

et al. 2021

IET Radar Sonar & Navi

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The target classification of unknown classes using radar sensor data is discussed. The neural network-based classifier shows high classification accuracy for the learned class targets. However, there is a risk of false decision for the untrained class target owing to an overconfidence problem. The output confidence of the classifier is calibrated using the deep convolutional neural network ensemble structure to propose a method to set the proper threshold for output confidence to decide unknown class targets. When using the proposed method, the accuracy of the learned target is maintained similar to that of the existing single neural network-based classifier, whereas the unknown class target is better identified. Further analysis verifies the effectiveness of the proposed method using commercial automotive radar. The proposed method can classify learned targets with an accuracy of 95% and distinguish unknown class targets with an accuracy of at least 85%. Based on the interaction with other sensors, individual sensors need to make reserved decisions about uncertain information. It is expected that the proposed ensemble network will be efficient in designing the classifier to perform target classification including unknown class decision.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…The radar-based hand gesture recognition method applies radar to collect the hand gesture signal, and analyzes the hand gesture information through the signal processing, and then classifies the hand gestures. Compared with the two aforementioned ones, the radar-based hand gesture recognition method performs in a non-contact way and brings a good user experience, as a result, it attracts extensive attention in both industry [14] and academic [15,16]. In [15], the authors adopt the radar to measure the range, speed and angle information of the hand gestures.…”

Section: Introductionmentioning

confidence: 99%

“…In [15], the authors adopt the radar to measure the range, speed and angle information of the hand gestures. In the vehicle-mounted auxiliary control system [16], the authors apply the time-frequency analysis method on the beat signals of different hand gestures using a frequency modulated continuous wave (FMCW) millimeterwave radar. Moreover, the Google Soli project [17] designs a hand gesture recognition system using a 7 GHz bandwidth radar.…”

Section: Introductionmentioning

confidence: 99%

A Novel Detection and Recognition Method for Continuous Hand Gesture Using FMCW Radar

et al. 2020

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In this paper, a novel method for continuous hand gesture detection and recognition is proposed based on a frequency modulated continuous wave (FMCW) radar. Firstly, we adopt the 2-Dimensional Fast Fourier Transform (2D-FFT) to estimate the range and Doppler parameters of the hand gesture raw data, and construct the range-time map (RTM) and Doppler-time map (DTM). Meanwhile, we apply the Multiple Signal Classification (MUSIC) algorithm to calculate the angle and construct the angletime map (ATM). Secondly, a hand gesture detection method is proposed to segment the continuous hand gestures using a decision threshold. Thirdly, the central time-frequency trajectory of each hand gesture spectrogram is clustered using the k-means algorithm, and then the Fusion Dynamic Time Warping (FDTW) algorithm is presented to recognize the hand gestures. Finally, experiments show that the accuracy of the proposed hand gesture detection method can reach 96.17%. The hand gesture average recognition accuracy of the proposed FDTW algorithm is 95.83%, while its time complexity is reduced by more than 50%. INDEX TERMS FMCW radar, continuous hand gesture recognition, detection, time-frequency trajectory, FDTW.

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

Cited by 23 publications

References 24 publications

Sensors and System for Vehicle Navigation

Sensors and System for Vehicle Navigation

Radar target classification considering unknown classes using deep convolutional neural network ensemble

A Novel Detection and Recognition Method for Continuous Hand Gesture Using FMCW Radar

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