A Short-Range FMCW Radar-Based Approach for Multi-Target Human-Vehicle Detection

Tavanti, Emanuele; Rizik, Ali; Fedeli, Alessandro; Caviglia, Daniele D.; Randazzo, Andrea

doi:10.1109/tgrs.2021.3138687

Cited by 30 publications

(23 citation statements)

References 64 publications

(92 reference statements)

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“…However, image-based HVR methods are susceptible to light so that their performance can be degraded rapidly in the case of low-light conditions such as night, cloudy, haze, etc. To alleviate the limitations of image-based HVR methods in low-light scenes, the microwave radar-based HVR method is proposed ( Park et al, 2021 ; Singh et al, 2021 ; Tavanti et al, 2021 ). However, high-performance frequency-modulated continuous-wave (FMCW) radars come at a higher cost.…”

Section: Introductionmentioning

confidence: 99%

A lightweight attention deep learning method for human-vehicle recognition based on wireless sensing technology

Song

Chen²,

Chen³

et al. 2023

Front. Neurosci.

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Wireless sensing-based human-vehicle recognition (WiHVR) methods have become a hot spot for research due to its non-invasiveness and cost-effective advantages. However, existing WiHVR methods shows limited performance and slow execution time on human-vehicle classification task. To address this issue, a lightweight wireless sensing attention-based deep learning model (LW-WADL) is proposed, which consists of a CBAM module and several depthwise separable convolution blocks in series. LW-WADL takes raw channel state information (CSI) as input, and extracts the advanced features of CSI by jointly using depthwise separable convolution and convolutional block attention mechanism (CBAM). Experimental results show that the proposed model achieves 96.26% accuracy on the constructed CSI-based dataset, and the model size is only 5.89% of the state of the art (SOTA) model. The results demonstrate that the proposed model achieves better performance on WiHVR tasks while reducing the model size compared to SOTA model.

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Section: Introductionmentioning

confidence: 99%

A lightweight attention deep learning method for human-vehicle recognition based on wireless sensing technology

Song

Chen²,

Chen³

et al. 2023

Front. Neurosci.

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“…Various types of FMCW transceivers are available on the market that operate in the 24 GHz industrial, scientific, and medical (ISM) band [17][18][19][20][21][22]. Distance2Go is a 24 GHz radar based on the BGT24MTR11 transceiver chipset from Infineon [17].…”

Section: Introductionmentioning

confidence: 99%

“…These features make the radar suitable for various applications, such as motion detection, presence sensing, proximity sensing, etc. [18,19]. The EV-TINYRAD24G from Analog-Devices is a 24 GHz radar evaluation module that allows for the implementation and testing of radar-sensing applications [20].…”

Section: Introductionmentioning

confidence: 99%

A Double Fourier-Transform Imaging Algorithm for a 24 GHz FMCW Short-Range Radar

Cicchetti

Pisa

Piuzzi

et al. 2023

Sensors

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A frequency-modulated continuous-wave radar for short-range target imaging, assembling a transceiver, a PLL, an SP4T switch, and a serial patch antenna array, was realized. A new algorithm based on a double Fourier transform (2D-FT) was developed and compared with the delay and sum (DAS) and multiple signal classification (MUSIC) algorithms proposed in the literature for target detection. The three reconstruction algorithms were applied to simulated canonical cases evidencing radar resolutions close to the theoretical ones. The proposed 2D-FT algorithm exhibits an angle of view greater than 25° and is five times faster than DAS and 20 times faster than the MUSIC one. The realized radar shows a range resolution of 55 cm and an angular resolution of 14° and is able to correctly identify the positions of single and multiple targets in realistic scenarios, with errors lower than 20 cm.

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“…The CSI data acquired through commercial Wi-Fi devices are usually extreme in phase noises. Therefore, there is a need to impose restrictions on the scalability and then degrade the precision of tracking by examining the Doppler shift with the time–frequency analysis of the CSI [ 1 , 31 , 32 ]. The hardware imperfection usually causes this extreme phase noise within the CSI data [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Device-Free Tracking through Self-Attention Mechanism and Unscented Kalman Filter with Commodity Wi-Fi

Nkabiti

Chen

2023

Sensors

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Recent advancements in target tracking using Wi-Fi signals and channel state information (CSI) have significantly improved the accuracy and efficiency of tracking mobile targets. However, there remains a gap in developing a comprehensive approach that combines CSI, an unscented Kalman filter (UKF), and a sole self-attention mechanism to accurately estimate the position, velocity, and acceleration of targets in real-time. Furthermore, optimizing the computational efficiency of such approaches is necessary for their applicability in resource-constrained environments. To bridge this gap, this research study proposes a novel approach that addresses these challenges. The approach leverages CSI data collected from commodity Wi-Fi devices and incorporates a combination of the UKF and a sole self-attention mechanism. By fusing these elements, the proposed model provides instantaneous and precise estimates of the target’s position while considering factors such as acceleration and network information. The effectiveness of the proposed approach is demonstrated through extensive experiments conducted in a controlled test bed environment. The results exhibit a remarkable tracking accuracy level of 97%, affirming the model’s ability to successfully track mobile targets. The achieved accuracy showcases the potential of the proposed approach for applications in human-computer interactions, surveillance, and security.

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A Short-Range FMCW Radar-Based Approach for Multi-Target Human-Vehicle Detection

Cited by 30 publications

References 64 publications

A lightweight attention deep learning method for human-vehicle recognition based on wireless sensing technology

A lightweight attention deep learning method for human-vehicle recognition based on wireless sensing technology

A Double Fourier-Transform Imaging Algorithm for a 24 GHz FMCW Short-Range Radar

Device-Free Tracking through Self-Attention Mechanism and Unscented Kalman Filter with Commodity Wi-Fi

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