用生成式隐空间对齐提升毫米波雷达占用检测的可解释性
Generative Latent Alignment for Interpretable Radar Based Occupancy Detection in Ambient Assisted Living
- 结合轻量卷积变分自编码器与冻结的CLIP文本编码器，学习雷达范围-角度图的低维隐表示
- 通过与'空房间'和'有人存在'语义锚点对齐，实现空间定位可视化
- 适合关注隐私保护下智能环境感知的科研与工程人员
本文研究如何在智能家居辅助生活（AAL）场景中提升毫米波雷达存在检测的可解释性，以替代引发隐私担忧的摄像头传感。提出生成式隐空间对齐（GLA）框架，将轻量级卷积变分自编码器与冻结的CLIP文本编码器结合，学习雷达范围-角度（RA）热图的低维隐表示。该隐空间被软对齐至两个语义锚点：'空房间'与'有人存在'。在对齐的隐空间中应用Grad-CAM，可视化支持每类判断的空间区域。在自建毫米波雷达数据集上，定性观察显示'有人存在'类别产生与强RA回波重合的紧凑梯度激活块，而'空房间'样本则呈现弥散或无证据。消融实验使用无关文本提示时，重建与定位性能均下降，表明雷达特定语义锚点对生成有意义解释至关重要。
原文摘要 · Abstract (English)
In this work, we study how to make mmWave radar presence detection more interpretable for Ambient Assisted Living (AAL) settings, where camera-based sensing raises privacy concerns. We propose a Generative Latent Alignment (GLA) framework that combines a lightweight convolutional variational autoencoder with a frozen CLIP text encoder to learn a low-dimensional latent representation of radar Range-Angle (RA) heatmaps. The latent space is softly aligned with two semantic anchors corresponding to "empty room" and "person present", and Grad-CAM is applied in this aligned latent space to visualize which spatial regions support each presence decision. On our mmWave radar dataset, we qualitatively observe that the "person present" class produces compact Grad-CAM blobs that coincide with strong RA returns, whereas "empty room" samples yield diffuse or no evidence. We also conduct an ablation study using unrelated text prompts, which degrades both reconstruction and localization, suggesting that radar-specific anchors are important for meaningful explanations in this setting.
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