Abstract
This study presents the first comprehensive evaluation of spatial generalization techniques, which are essential for the practical deployment of deep learning-based radio-frequency (RF) sensing. Focusing on people counting in indoor environments using frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) radar, we systematically investigate a broad set of approaches, including amplitude-based statistical preprocessing (sigmoid weighting and threshold zeroing), frequency-domain filtering, autoencoder-based background suppression, data augmentation strategies, and transfer learning. Experimental results collected across two environments with different layouts demonstrate that sigmoid-based amplitude weighting consistently achieves superior cross-environment performance, yielding 50.1% and 55.2% reductions in root-mean-square error (RMSE) and mean absolute error (MAE), respectively, compared with baseline methods. Data augmentation provides additional though modest benefits, with improvements up to 8.8% in MAE. By contrast, transfer learning proves indispensable for large spatial shifts, achieving 82.1% and 91.3% reductions in RMSE and MAE, respectively, with 540 target-domain samples. Taken together, these findings establish a highly practical direction for developing radar sensing systems capable of maintaining robust accuracy under spatial variations by integrating deep learning models with amplitude-based preprocessing and efficient transfer learning.
Abstract (translated)
这项研究首次全面评估了空间泛化技术，这些技术对于基于深度学习的射频（RF）传感的实际部署至关重要。本研究专注于使用频率调制连续波（FMCW）多输入多输出（MIMO）雷达在室内环境中进行人员计数，并系统地调查了一整套方法，包括基于幅度的统计预处理（sigmoid加权和阈值置零）、频域滤波、基于自动编码器的背景抑制、数据增强策略以及迁移学习。 实验结果来自两个具有不同布局的环境，在这些环境中收集的数据表明，基于sigmoid的幅度加权在跨环境性能方面始终优于基线方法，分别将均方根误差（RMSE）和平均绝对误差（MAE）降低了50.1% 和 55.2%。数据增强虽然提供了额外但相对较小的好处，在平均绝对误差上最多提高了8.8%。相比之下，迁移学习对于大的空间变化是必不可少的，在仅有540个目标领域样本的情况下，分别实现了RMSE和MAE减少了82.1%和91.3%。 综合来看，这些发现确立了一条高度实用的方向，通过将深度学习模型与基于幅度的预处理以及高效的迁移学习相结合，开发出能够在空间变化下保持稳健准确性的雷达传感系统。
URL
https://arxiv.org/abs/2512.13018
