Differentiable Radio Frequency Ray Tracing for Millimeter-Wave Sensing

Millimeter wave (mmWave) sensing is an emerging technology with applications in 3D object characterization and environment mapping. However, realizing precise 3D reconstruction from sparse mmWave signals remains challenging. Existing methods rely on data-driven learning, constrained by dataset avail...

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Bibliographic Details
Published inarXiv.org
Main Authors Chen, Xingyu, Zhang, Xinyu, Xia, Qiyue, Fang, Xinmin, Lu, Chris Xiaoxuan, Li, Zhengxiong
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 22.11.2023
Subjects
Millimeter waves
New technology
Radar
Ray tracing
Reconstruction
Simulation
Three dimensional models
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Summary:Millimeter wave (mmWave) sensing is an emerging technology with applications in 3D object characterization and environment mapping. However, realizing precise 3D reconstruction from sparse mmWave signals remains challenging. Existing methods rely on data-driven learning, constrained by dataset availability and difficulty in generalization. We propose DiffSBR, a differentiable framework for mmWave-based 3D reconstruction. DiffSBR incorporates a differentiable ray tracing engine to simulate radar point clouds from virtual 3D models. A gradient-based optimizer refines the model parameters to minimize the discrepancy between simulated and real point clouds. Experiments using various radar hardware validate DiffSBR's capability for fine-grained 3D reconstruction, even for novel objects unseen by the radar previously. By integrating physics-based simulation with gradient optimization, DiffSBR transcends the limitations of data-driven approaches and pioneers a new paradigm for mmWave sensing.
ISSN:2331-8422