Optical Filter-Less Photonic FMCW Radar for Multi-Target Detection

Dhawan, Rajveer; Moyal, Kartik; Choudhary, Amol

doi:10.1109/lpt.2022.3224219

Cited by 10 publications

(1 citation statement)

References 16 publications

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“…To tackle this challenge, this study employed a linear regression approach based on a visual model trained to predict human poses from throughwall radar image data. This research leveraged a cross-modal teacher-student network [18][19][20] to transfer learned knowledge between the two distinct modalities of the camera dataset and radar dataset. This approach facilitates comprehensive knowledge transfer, including insights into dense keypoint confidence maps [21,22].…”

Section: Cross-modal Supervision Methodsmentioning

confidence: 99%

Cross-Modal Supervised Human Body Pose Recognition Techniques for Through-Wall Radar

Xu,

Liu,

Wang

et al. 2024

Sensors

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Through-wall radar human body pose recognition technology has broad applications in both military and civilian sectors. Identifying the current pose of targets behind walls and predicting subsequent pose changes are significant challenges. Conventional methods typically utilize radar information along with machine learning algorithms such as SVM and random forests to aid in recognition. However, these approaches have limitations, particularly in complex scenarios. In response to this challenge, this paper proposes a cross-modal supervised through-wall radar human body pose recognition method. By integrating information from both cameras and radar, a cross-modal dataset was constructed, and a corresponding deep learning network architecture was designed. During training, the network effectively learned the pose features of targets obscured by walls, enabling accurate pose recognition (e.g., standing, crouching) in scenarios with unknown wall obstructions. The experimental results demonstrated the superiority of the proposed method over traditional approaches, offering an effective and innovative solution for practical through-wall radar applications. The contribution of this study lies in the integration of deep learning with cross-modal supervision, providing new perspectives for enhancing the robustness and accuracy of target pose recognition.

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Section: Cross-modal Supervision Methodsmentioning

confidence: 99%

Cross-Modal Supervised Human Body Pose Recognition Techniques for Through-Wall Radar

Xu,

Liu,

Wang

et al. 2024

Sensors

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Multiband dual- and cross-LFM waveform generation using a dual-drive Mach–Zehnder modulator

Dhawan

Parihar

Choudhary

2023

Optics Communications

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Photonic generation of Barker encoded dual-nonlinear frequency modulated RADAR waveforms

Dhawan,

Choudhary

2023

Results in Optics

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Optical Filter-Less Photonic FMCW Radar for Multi-Target Detection

Cited by 10 publications

References 16 publications

Cross-Modal Supervised Human Body Pose Recognition Techniques for Through-Wall Radar

Cross-Modal Supervised Human Body Pose Recognition Techniques for Through-Wall Radar

Multiband dual- and cross-LFM waveform generation using a dual-drive Mach–Zehnder modulator

Photonic generation of Barker encoded dual-nonlinear frequency modulated RADAR waveforms

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Optical Filter-Less Photonic FMCW Radar for Multi-Target Detection

Cited by 10 publications

References 16 publications

Cross-Modal Supervised Human Body Pose Recognition Techniques for Through-Wall Radar

Cross-Modal Supervised Human Body Pose Recognition Techniques for Through-Wall Radar

Multiband dual- and cross-LFM waveform generation using a dual-drive Mach–Zehnder​ modulator

Photonic generation of Barker encoded dual-nonlinear frequency modulated RADAR waveforms

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Product

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Multiband dual- and cross-LFM waveform generation using a dual-drive Mach–Zehnder modulator