Classical DOA Algorithm Series: MUSIC - Noise Subspace Method

This article provides an in-depth exploration of the classical MUSIC algorithm, specifically focusing on the noise subspace method. While numerous improvements have been developed for this algorithm, we present a novel enhanced version - MUSIC Series 2. This algorithm implements the noise subspace approach with critical enhancements in signal processing efficiency.

The MUSIC Series 2 algorithm demonstrates superior noise resistance during signal processing operations, enabling more accurate source signal localization. Implementation typically involves computing the signal covariance matrix, performing eigenvalue decomposition to separate signal and noise subspaces, and constructing the MUSIC spectrum using noise eigenvectors. Key computational advantages include optimized matrix operations and efficient eigenvalue sorting algorithms.

Notable improvements include higher computational efficiency through optimized subspace decomposition techniques and enhanced stability via robust covariance matrix estimation. These advancements make MUSIC Series 2 particularly valuable for real-time signal processing applications. We anticipate this algorithm will play a significant role in signal processing domains and represent an important research direction for future developments in array signal processing.