An Unsupervised Domain Adaptation-Based Device-Free Sensing Approach for Cross-Weather Target Recognition in Foliage Environments

In recent years, the emerging technique of device-free sensing (DFS) has gained popularity for foliage penetration (FOPEN) target recognition. This popularity is primarily attributed to its inherent advantage of not requiring specialized sensing equipment beyond wireless transceivers. Concerning weather variations, DFS heavily relies on labeled data for model training, which necessitates the annotation of samples for each weather environment. However, this annotation process proves impractical for real-world applications, especially under adverse weather conditions. To address this issue, this paper presents an unsupervised domain adaptation (UDA)-based cross-weather FOPEN target recognition system (CW-FTRS). Experimental results validate that the proposed method achieves an average accuracy of over 72% in unseen weather conditions using only unlabeled data samples.