Conventional DOA Spectrum Estimation Using SVD Algorithm

In multipath environments, conventional Direction of Arrival (DOA) spectrum estimation using the MUSIC algorithm fails to perform effectively. To address this limitation, the Singular Value Decomposition (SVD) algorithm provides a robust solution. The SVD algorithm reconstructs the singular value matrix by utilizing eigenvectors associated with the largest eigenvalues, enabling successful estimation of coherent signals. This approach demonstrates remarkable effectiveness by not only enhancing estimation accuracy but also maintaining stability under signal degradation conditions. Implementation typically involves computing the covariance matrix from received data, performing SVD decomposition using functions like numpy.linalg.svd() in Python or svd() in MATLAB, and reconstructing the signal subspace from dominant singular vectors. Furthermore, the SVD algorithm's excellent scalability makes it suitable for widespread application across various scenarios, including wireless communications and radar systems.