Incremental Wavelet-Capsules: A Cross-Environment Solution for WiFi Identification

WiFi-based identity recognition differs from traditional identification technologies as it is not limited by lighting conditions and does not require dense, specialized sensors or wearable devices. This makes it valuable in modern human–machine interactions. However, the diversity of real-world environmental conditions substantially limits the application of existing WiFi-based identity recognition algorithms, particularly when applied across different environments. As a solution, we introduce the incremental wavelet capsule (IWC) model, which combines a newly designed wavelet convolution layer with a capsule network to accelerate precise feature extraction. We adopt a hybrid incremental learning strategy, solving the catastrophic forgetting<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> problem in cross-environment tasks and enabling the model to adapt to new environments in the data stream without forgetting the original environment. Furthermore, we developed a customized data augmentation method for WiFi signals, enhancing the model’s adaptability and stability across various environments. Experimental results show that the IWC model achieves an average recognition accuracy of 97.36% across five different environments and maintains an accuracy of 91.5% even when only 5% of the training data from a new environment is used. These findings demonstrate the model’s robust performance and practicality in cross-environment scenarios.