Independent Component Analysis (ICA) is a statistical signal processing technique that decomposes a multivariate signal into maximally statistically independent source components, based on the assumption that the observed signals are linear mixtures of underlying independent sources. In WiFi CSI-based sensing, ICA is used to separate and extract meaningful motion-related signals from the complex mixture of noise, multipath interference, and environmental clutter captured across antenna pairs, thereby improving the reliability of subsequent detection or recognition tasks. Its significance lies in its ability to perform blind source separation without prior knowledge of the mixing process, making it well-suited for the uncontrolled wireless propagation environments typical of device-free sensing scenarios.
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