Toward Ant-Sized Moving Object Localization Using Deep Learning in FMCW Radar: A Pilot Study

Kumchaiseemak, Nakorn; Chatnuntawech, Itthi; Teerapittayanon, Surat; Kotchapansompote, Palakon; Kaewlee, Thitikorn; Piriyajitakonkij, Maytus; Wilaiprasitporn, Theerawit; Suwajanakorn, Supasorn

doi:10.1109/tgrs.2022.3169642

Cited by 7 publications

(2 citation statements)

References 47 publications

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“…Table I presents some of the studies that have recently investigated 3D radar-based imaging at THz frequencies. The capabilities of radar based 3D imaging and sensing has also been recently investigated for various applications including topographic mapping [48], deep learning based approach for moving object localization [49], indoor human vital signs and gesture/activity/posture localization, monitoring and classification [50], [51] and other applications in the THz, radio and microwave frequencies [52]- [60]. The capability of SAR technology to achieve 3D imaging and the exploration of efficient reconstruction algorithms in THz and Microwave frequency bands have been reported in various studies including [40], [61]- [65].…”

Section: B Related Workmentioning

confidence: 99%

Curved Synthetic Aperture Radar for Near-Field Terahertz Imaging

Gezimati

Singh

2023

IEEE Photonics J.

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High resolution imaging technique will play an essential role in the future smart imaging applications to provide potential solutions for accurate detection, monitoring and classification of the target. The potential of synthetic aperture radar (SAR) systems to acquire high resolution images has opened new frontiers and led to its exploration in new applications. The conventional microwave radar imaging suffers limited range resolution in the sub-millimeter wave range and optical imaging methods are constrained by the diffraction limit. In the terahertz (THz) regime of the spectrum, the SAR systems could be operated in all the weather, all the time and are capable of penetrating through clouds, smoke, dust etc. to achieve high resolution images beyond the diffraction limit. The curved SAR presents a SAR system created by circularly encircling the target scene. The Circular SAR and Spiral SAR which are based on a circular and cylindrical spiral scanning trajectory, respectively are the scanning methods used for curved SAR. In this paper, we have explored a new toolbox that enables the rapid development of near-field THz-SAR imaging systems and generation of large near-field THz imaging scenarios with the goal to support and facilitate data driven research such as deep learning in the THz community through synthetically generated SAR images as well as its potential for THz based high resolution and 2D/3D near-field imaging application. Initially, the potential advantages of THz-SAR over Microwave SAR and optical imaging methods are presented. Further, a frequency modulated continuous wave (FMCW) radar testbed is simulated in multi-input multi-output (MIMO) configuration for the target frequency band. Moreover, the circular and cylindrical SAR scanning methods are explored for near-field imaging of point targets to obtain 2D and 3D THz-SAR images, respectively. Also, the radar images are reconstructed using the Back Propagation and Polar Formatting Algorithms, focusing on short range applications like indoor environments. Finally, the SAR performance evaluation metrics are reported and a roadmap for future work is presented.

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Section: B Related Workmentioning

confidence: 99%

Curved Synthetic Aperture Radar for Near-Field Terahertz Imaging

Gezimati

Singh

2023

IEEE Photonics J.

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“…Deep learning (DL) methods have recently emerged as promising approaches for direction-of-arrival (DOA) estimation, offering significant advantages over traditional subspace and sparse methods [9,10]. For DOA estimation of multitarget in harsh environments, multi-layer perceptron (MLP) method focuses on the robustness to array imperfections [11]; however, the model is trained at each individual SNR and fixed on a two-source target.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Convolutional Neural Network with Supervised Contrastive Learning for Multi-Target DOA Estimation in Low SNR

Li,

Zhou,

Chen

et al. 2023

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In this paper, a modified high-efficiency Convolutional Neural Network (CNN) with a novel Supervised Contrastive Learning (SCL) approach is introduced to estimate direction-of-arrival (DOA) of multiple targets in low signal-to-noise ratio (SNR) regimes with uniform linear arrays (ULA). The model is trained using an on-grid setting, and thus the problem is modeled as a multi-label classification task. Simulation results demonstrate the robustness of the proposed approach in scenarios with low SNR and a small number of snapshots. Notably, the method exhibits strong capability in detecting the number of sources while estimating their DOAs. Furthermore, compared to traditional CNN methods, our refined efficient CNN significantly reduces the number of parameters by a factor of sixteen while still achieving comparable results. The effectiveness of the proposed method is analyzed through the visualization of latent space and through the advanced theory of feature learning.

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Single 24-GHz FMCW Radar-Based Indoor Device-Free Human Localization and Posture Sensing With CNN

Yang

Kim

2023

IEEE Sensors J.

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Toward Ant-Sized Moving Object Localization Using Deep Learning in FMCW Radar: A Pilot Study

Cited by 7 publications

References 47 publications

Curved Synthetic Aperture Radar for Near-Field Terahertz Imaging

Curved Synthetic Aperture Radar for Near-Field Terahertz Imaging

An Efficient Convolutional Neural Network with Supervised Contrastive Learning for Multi-Target DOA Estimation in Low SNR

Single 24-GHz FMCW Radar-Based Indoor Device-Free Human Localization and Posture Sensing With CNN

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