root-MUSIC Algorithm

In signal processing, array signal processing represents a crucial methodology that utilizes a set of sensors (arrays) to receive signals. By analyzing differences between sensors, this approach enhances signal processing quality and precision. Classic array signal processing algorithms include beamforming, spatial spectrum estimation, and direction of arrival (DOA) estimation. Beamforming techniques control signal transmission direction by adjusting sensor weights and phases, typically implemented through covariance matrix calculation and weight vector optimization. Spatial spectrum estimation employs algorithms like MUSIC or ESPRIT to spatially localize signal sources, involving eigenvalue decomposition of covariance matrices and peak detection in spectrum plots. Direction estimation determines signal transmission directions, with applications in radar, sonar, and communication systems. The root-MUSIC algorithm specifically enhances DOA estimation performance by converting spectral peaks to polynomial roots, providing higher resolution than conventional methods through root-solving techniques applied to the noise subspace. Notably, research and applications in array signal processing continue to evolve and expand, bringing increased convenience and innovation to various aspects of modern life and professional fields. Implementation typically involves MATLAB or Python libraries for array manifold modeling, covariance matrix computation, and subspace decomposition operations.