Moving Target Localization and Activity/Gesture Recognition for Indoor Radio Frequency Sensing Applications

Sun, Yingxiang; Xiong, Haoqiu; Tan, Danny Kai Pin; Han, Tony Xiao; Du, Rui; Yang, Xun; Ye, Terry Tao

doi:10.1109/jsen.2021.3111187

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…In this section, we summarize some related works about human posture or activity recognition and localization applications in indoor environments. In the first stage of research on localization, researchers investigated active schemes that require the target to carry a wearable device, such as a motion sensor or beacon [30][31][32][33][34][35]. For instance, Wang et al [33] proposed a single person fall detection system, namely WiFall, which can be used to monitor elderly people with physical conditions when they stay at home alone.…”

Section: Related Workmentioning

confidence: 99%

“…Bhattacharya et al [34] developed a fall detection and breathing sensing technique based on radar technique to detect a fall after it has happened even when the person is static. Sun et al [35] proposed a RF sensing method to achieve joint moving target localization and activity or gesture recognition by a dual-frequency continuous wave radar.…”

Section: Related Workmentioning

confidence: 99%

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Deep Learning-Based Device-Free Localization Scheme for Simultaneous Estimation of Indoor Location and Posture Using FMCW Radars

Lee

Park

Kim

2022

Sensors

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Indoor device-free localization (DFL) systems are used in various Internet-of-Things applications based on human behavior recognition. However, the usage of camera-based intuitive DFL approaches is limited in dark environments and disaster situations. Moreover, camera-based DFL schemes exhibit certain privacy issues. Therefore, DFL schemes with radars are increasingly being investigated owing to their efficient functioning in dark environments and their ability to prevent privacy issues. This study proposes a deep learning-based DFL scheme for simultaneous estimation of indoor location and posture using 24-GHz frequency-modulated continuous-wave (FMCW) radars. The proposed scheme uses a parallel 1D convolutional neural network structure with a regression and a classification model for localization and posture estimation, respectively. The two-dimensional location information of the target is estimated for localization, and four different postures, namely standing, sitting, lying, and absence, are estimated simultaneously. We experimentally evaluated the proposed scheme and compared its performance with that of conventional schemes under identical conditions. The results indicate that the average localization error of the proposed scheme is 0.23 m, whereas that of the conventional scheme is approximately 0.65 m. The average posture estimation error of the proposed scheme is approximately 1.7%, whereas that of the conventional correlation, CSP, and SVM schemes are 54.8%, 42%, and 10%, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Device-Free Localization Scheme for Simultaneous Estimation of Indoor Location and Posture Using FMCW Radars

Lee

Park

Kim

2022

Sensors

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“…While motion detection can be done with a human-in-the-loop approach, this is not desirable in automate, stand-alone systems. Instead, a power-based automated segmentation algorithm, such as short time average over long time average (STA/LTA) [56], [57], dynamic boundary detection (DBD) [58] or power burst curve [59] (PBC) may be utilized.…”

Section: Motion Detection and Segmentationmentioning

confidence: 99%

ASL Trigger Recognition in Mixed Activity/Signing Sequences for RF Sensor-Based User Interfaces

Kurtoğlu¹,

Gürbüz²,

Malaia³

et al. 2021

Preprint

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“…In modern electromagnetic spectrum warfare, radar target recognition has attracted more and more attentions [1][2][3][4]. Identification of the enemy targets accurately and timely, especially the stealth aircrafts, is the prerequisite for destroying the enemy targets, and any confusion between civilian or friendly targets and enemy targets may cause tragedy [5].…”

Section: Introductionmentioning

confidence: 99%

Pole Extraction of Radar Target in Resonant Region Based on Sliding-Window Matrix Pencil Method

Deng

Zhang

2022

Wireless Communications and Mobile Computing

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In the complex battlefield environment, stealth radar target recognition has been paid increasing attentions. Previous studies have demonstrated that the stealth target can be identified well by pole extraction based on matrix pencil method (MPM). However, MPM suffers from the difficulty in setting model order and the time-domain resonant response aliasing problem. This paper proposes a new sliding-window matrix pencil method (SW-MPM) based on dynamic order setting and sliding window. Dynamic order setting scheme is used to overcome the difficulty of setting the order in matrix pencil method, while sliding window can avoid the aliasing problem of time-domain resonant response to some extent. The time-domain scattering data used in SW-MPM are obtained by inverse Fourier transform of the frequency domain scattering data in the resonance region. Taking typical stealth aircraft identification as example, the simulation results verify that the proposed method may extract more number of poles with better azimuth consistency, which is beneficial to improve the accuracy of pole-based target identification.

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Moving Target Localization and Activity/Gesture Recognition for Indoor Radio Frequency Sensing Applications

Cited by 5 publications

References 33 publications

Deep Learning-Based Device-Free Localization Scheme for Simultaneous Estimation of Indoor Location and Posture Using FMCW Radars

Deep Learning-Based Device-Free Localization Scheme for Simultaneous Estimation of Indoor Location and Posture Using FMCW Radars

ASL Trigger Recognition in Mixed Activity/Signing Sequences for RF Sensor-Based User Interfaces

Pole Extraction of Radar Target in Resonant Region Based on Sliding-Window Matrix Pencil Method

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