DOA Estimation - Classic MUSIC Algorithm Source Code Implementation

This article provides a comprehensive introduction to array signal processing and Direction of Arrival (DOA) estimation, with particular focus on the implementation details of the classic MUSIC (Multiple Signal Classification) algorithm source code. Array signal processing is a widely used technique that utilizes multiple sensors to receive signals and combines them to enhance signal quality while reducing noise interference. DOA estimation represents a crucial application of array signal processing, enabling the determination of signal source directions, which plays a vital role in fields such as radar systems, wireless communications, and acoustic engineering. In DOA estimation, the MUSIC algorithm stands as one of the most prominent techniques. It employs eigenvalue decomposition of the array covariance matrix to estimate signal source directions. The classic MUSIC source code implementation typically involves several key steps: first computing the sample covariance matrix from received array data, then performing eigenvalue decomposition to separate signal and noise subspaces, and finally constructing the MUSIC spatial spectrum by exploiting the orthogonality between steering vectors and noise subspace. The algorithm implementation commonly utilizes mathematical operations such as covariance matrix calculation (using functions like cov() in MATLAB), eigenvalue decomposition (via eig() or svd() functions), and peak detection in the spatial spectrum to identify DOA estimates. This implementation finds extensive applications in antenna array processing, acoustic source localization, and biomedical imaging systems. Therefore, understanding the fundamentals of array signal processing and DOA estimation, along with the practical implementation aspects of the classic MUSIC algorithm source code, is essential for professionals and students working in signal processing, communications, radar systems, and acoustics-related fields.