用雷达相位数据直接建模呼吸信号，高效去噪且精度高
mmWave-Diffusion:A Novel Framework for Respiration Sensing Using Observation-Anchored Conditional Diffusion Model
- 基于雷达相位观测构建条件扩散模型，从真实数据附近采样
- 20步逆向生成即可完成呼吸波形重建，呼吸率估计准确率超当前最优
- 适合做非接触式生命体征监测的工程师和医疗科研人员
毫米波雷达可实现无接触呼吸监测，但身体微动作带来的非平稳干扰常导致精细监测性能下降。为去除微动作干扰，本文提出mmWave-Diffusion：一种基于观测锚定的条件扩散框架，直接建模雷达相位观测与呼吸真实信号之间的残差，并在观测一致邻域内初始化采样，而非使用高斯噪声，使生成过程更符合测量物理特性并降低推理开销。配套的Radar Diffusion Transformer（RDT）显式依赖相位观测，通过补丁级双位置编码保证严格时序一一对应，并利用带状掩码多头交叉注意力注入局部物理先验，仅需20步逆向过程即可实现鲁棒去噪与干扰消除。在13.25小时同步雷达-呼吸数据上评估，该方法在波形重建与呼吸率估计上达到当前最佳性能，具备强泛化能力。代码已开源：https://github.com/goodluckyongw/mmWave-Diffusion。
原文摘要 · Abstract (English)
Millimeter-wave (mmWave) radar enables contactless respiratory sensing,yet fine-grained monitoring is often degraded by nonstationary interference from body micromotions.To achieve micromotion interference removal,we propose mmWave-Diffusion,an observation-anchored conditional diffusion framework that directly models the residual between radar phase observations and the respiratory ground truth,and initializes sampling within an observation-consistent neighborhood rather than from Gaussian noise-thereby aligning the generative process with the measurement physics and reducing inference overhead. The accompanying Radar Diffusion Transformer (RDT) is explicitly conditioned on phase observations, enforces strict one-to-one temporal alignment via patch-level dual positional encodings, and injects local physical priors through banded-mask multi-head cross-attention, enabling robust denoising and interference removal in just 20 reverse steps. Evaluated on 13.25 hours of synchronized radar-respiration data, mmWave-Diffusion achieves state-of-the-art waveform reconstruction and respiratory-rate estimation with strong generalization. Code repository:https://github.com/goodluckyongw/mmWave-Diffusion.
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