MUSIC Algorithm Based on Fourth-Order Cumulants

In modern communication systems, array signal processing serves as a critical technology that enables extraction of desired information from received signals while suppressing unwanted interference. The MUSIC (Multiple Signal Classification) algorithm represents a widely-used array signal processing technique that leverages the relative positional relationships among array elements to perform spatial spectrum analysis and calculation, thereby achieving accurate signal source localization. The fourth-order cumulant-based MUSIC algorithm enhances traditional MUSIC by significantly improving processing accuracy and stability, making it particularly suitable for complex signal scenarios involving non-Gaussian noise and coherent sources. From an implementation perspective, this approach involves constructing a fourth-order cumulant matrix instead of the standard covariance matrix, which requires specialized functions for cumulant calculation and advanced eigenvalue decomposition techniques. The algorithm typically includes steps for cumulant matrix formation, signal subspace identification through eigenvalue decomposition, and peak detection in the spatial spectrum. Consequently, array signal processing using the fourth-order cumulant MUSIC algorithm constitutes an important research domain with broad application prospects in radar systems, wireless communications, and acoustic signal processing.