Comprehensive Deployment-Oriented Assessment for Cross-Environment Generalization in Deep Learning-Based mmWave Radar Sensing

TL;DR

This paper evaluates various spatial generalization techniques for deep learning-based mmWave radar sensing, demonstrating that amplitude-based preprocessing combined with transfer learning significantly improves cross-environment accuracy in indoor people counting.

Contribution

It provides a comprehensive assessment of spatial generalization methods, highlighting the effectiveness of sigmoid-based amplitude weighting and transfer learning for robust radar sensing across environments.

Findings

Sigmoid-based amplitude weighting reduces RMSE by 50.1% and MAE by 55.2%.

Data augmentation improves MAE by up to 8.8%.

Transfer learning achieves over 80% reduction in errors with 540 samples.

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.