MUSIC (Multiple Signal Classification) is a subspace-based spectral estimation algorithm that decomposes the signal covariance matrix into orthogonal signal and noise subspaces to achieve super-resolution estimation of parameters such as angle of arrival (AoA), time of flight (ToF), and frequency of arrival in WiFi/CSI sensing systems. It matters to the field because it significantly surpasses the resolution limits of classical Fourier-based methods, enabling fine-grained localization and sensing even when multiple signal paths or targets are closely spaced. Key variants include Root-MUSIC, which reformulates the spectral search as a polynomial rooting problem for improved computational efficiency, and ESPRIT, a closely related subspace method that avoids the explicit spectral search entirely, with both approaches frequently applied in antenna array processing and multipath parameter extraction pipelines.
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