Sparse DOA Estimation via Covariance Matrix with Implementation Insights

When employing compressed sensing for localization tasks, sparse DOA (Direction of Arrival) estimation through covariance matrices represents a valuable reference technique. This methodology builds upon DOA principles to calculate angular relationships between sensors and target sources. In DOA implementations, the covariance matrix serves to determine signal directionality, while in compressed sensing frameworks this matrix can be sparsified and compressed into a reduced-dimensional representation, thereby enhancing computational efficiency. The technique typically involves constructing the spatial covariance matrix from sensor array data, applying sparse recovery algorithms like L1-norm optimization or orthogonal matching pursuit to identify dominant directions, and implementing eigenvalue decomposition for signal subspace identification. Practical implementation often utilizes MATLAB functions such as eig() for eigenvalue computation and l1-minimization solvers for sparse recovery. Consequently, covariance matrix sparse DOA estimation stands as a practical approach that accelerates localization processes while maintaining estimation accuracy.