Discrete Wavelet Transform (DWT) is a signal processing technique that decomposes a CSI time-series signal into multiple frequency sub-bands at different resolutions by applying successive high-pass and low-pass filter banks, capturing both temporal and spectral information simultaneously. In WiFi CSI-based sensing, DWT matters because it enables effective denoising, feature extraction, and multi-scale analysis of human activity signals without the stationarity assumptions required by Fourier-based methods, making it well-suited for the non-stationary, transient nature of motion-induced channel variations. Key variants used in the field include multi-level DWT decomposition, where signals are iteratively decomposed across several scales to isolate coarse approximation coefficients from fine detail coefficients, and the stationary (undecimated) wavelet transform, which preserves temporal alignment by omitting downsampling and is particularly useful when precise localization of signal events in time is required.
Source Papers
- A Survey on Green Wireless Sensing: Energy-Efficient Sensing via WiFi CSI and Lightweight Learning ↗ — A Survey on Green Wireless Sensing: Energy-Efficient Sensing
- A Survey on Wireless Device-free Human Sensing: Application Scenarios, Current Solutions, and Open Issues ↗ — A Survey on Wireless Device-free Human Sensing: Application
- On CSI and Passive Wi-Fi Radar for Opportunistic Physical Activity Recognition ↗ — On CSI and Passive Wi-Fi Radar for Opportunistic Physical Ac
- Understanding and Modeling of WiFi Signal Based Human Activity Recognition ↗ — Understanding and Modeling of WiFi Signal Based Human Activi
- WiFi Sensing with Channel State Information ↗ — WiFi Sensing with Channel State Information
- WiFi-Based Human Sensing With Deep Learning: Recent Advances, Challenges, and Opportunities ↗ — WiFi-Based Human Sensing With Deep Learning: Recent Advances