Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar

Park, Kyungeun; Lee, Jeongpyo; Kim, Young‐Ok

doi:10.3390/electronics10172166

Cited by 9 publications

(19 citation statements)

References 20 publications

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“…To address these drawbacks of active schemes and extend the possible applications of localization systems, DFL schemes using cameras or radio signals, such as Wi-Fi, Zigbee, RFID, Bluetooth, and FMCW radars, were introduced [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. 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.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, certain privacy issues are associated with camera-based DFL schemes. Therefore, DFL schemes with radars [16][17][18] were considered, as they are known to prevent privacy issues and function efficiently even in dark or smoky environments.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, AI is employed in DFL schemes, exploiting FMCW radars to enhance the accuracy of distance estimation [29]. Recently, a DL-based indoor two-dimensional (2D) localization scheme was introduced using a 24-GHz FMCW radar to address the limitations of the conventional 2D localization scheme based on multilateration methods [18].…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…In recent years, deep learning algorithms such as deep neural networks (DNNs) have achieved great success in a wide range of artificial intelligence (AI) applications, including, but not limited to, image recognition, natural language processing, and pattern recognition [1][2][3][4][5][6][7]. To obtain high computing performance, current DNNs are mainly implemented or accelerated by Von Neumann computer architecture based on traditional circuits such as CPU, GPU, and FPGA [8,9].…”

Section: Introductionmentioning

confidence: 99%

A Highly Robust Binary Neural Network Inference Accelerator Based on Binary Memristors

Zhao

Wang

et al. 2021

Electronics

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Since memristor was found, it has shown great application potential in neuromorphic computing. Currently, most neural networks based on memristors deploy the special analog characteristics of memristor. However, owing to the limitation of manufacturing process, non-ideal characteristics such as non-linearity, asymmetry, and inconsistent device periodicity appear frequently and definitely, therefore, it is a challenge to employ memristor in a massive way. On the contrary, a binary neural network (BNN) requires its weights to be either +1 or −1, which can be mapped by digital memristors with high technical maturity. Upon this, a highly robust BNN inference accelerator with binary sigmoid activation function is proposed. In the accelerator, the inputs of each network layer are either +1 or 0, which can facilitate feature encoding and reduce the peripheral circuit complexity of memristor hardware. The proposed two-column reference memristor structure together with current controlled voltage source (CCVS) circuit not only solves the problem of mapping positive and negative weights on memristor array, but also eliminates the sneak current effect under the minimum conductance status. Being compared to the traditional differential pair structure of BNN, the proposed two-column reference scheme can reduce both the number of memristors and the latency to refresh the memristor array by nearly 50%. The influence of non-ideal factors of memristor array such as memristor array yield, memristor conductance fluctuation, and reading noise on the accuracy of BNN is investigated in detail based on a newly memristor circuit model with non-ideal characteristics. The experimental results demonstrate that when the array yield α ≥ 5%, or the reading noise σ ≤ 0.25, a recognition accuracy greater than 97% on the MNIST data set is achieved.

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“…These radars are widely employed in IoT applications because they do not require high computing power and data acquisition devices. These characteristics of the FMCW radar facilitate the design of low-cost, compact radar systems [16][17][18][19][20]. For instance, a simple FMCW radar system is proposed, but it is built using a high-cost lumped standalone RF component and audio input port of a PC in [21].…”

Section: Introductionmentioning

confidence: 99%

Educational Low-Cost C-Band FMCW Radar System Comprising Commercial Off-the-Shelf Components for Indoor Through-Wall Object Detection

Jeong

Kim

2021

Electronics

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This paper presents an educational low-cost C-band frequency-modulated continuous wave (FMCW) radar system for use in indoor through-wall metal detection. Indoor remote-sensing applications, such as through-wall detection and positioning, are essential for the comprehensive realization of the internet of things or super-connected societies. The proposed system comprises a two-stage radio-frequency power amplifier, a voltage-controlled oscillator, circuits for frequency modulation and system synchronization, a mixer, a 3-dB power divider, a low-noise amplifier, and two cylindrical horn antennas (Tx/Rx antennas). The antenna yields gain values in the 6.8~7.8 range when operating in the 5.83~5.94 GHz frequency band. The backscattered Tx signal is sampled at 4.5 kHz using the Arduino UNO analog-to-digital converter. Thereafter, the sampled signal is transferred to the MATLAB platform and analyzed using a customized FMCW radar algorithm. The proposed system is built using commercial off-the-shelf components, and it can detect targets within a 56.3 m radius in indoor environments. In this study, the system could successfully detect targets through a 4 cm-thick ply board with a measurement accuracy of less than 10 cm.

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Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar

Cited by 9 publications

References 20 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

A Highly Robust Binary Neural Network Inference Accelerator Based on Binary Memristors

Educational Low-Cost C-Band FMCW Radar System Comprising Commercial Off-the-Shelf Components for Indoor Through-Wall Object Detection

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