1990/07/01 by P. Stoica, Petre Stoica, K.C. Sharman · 899 citations
Computer Science · Earth and Planetary Sciences · Mathematics · #Algorithm #Artificial intelligence #Computer science #Direction of arrival #Direction-of-Arrival Estimation Techniques #Eigenvalues and eigenvectors #Mathematics #Maximum likelihood #Mode (computer interface) #Multiple signal classification #Noise (video) #Realization (probability) #Sample (material) #Sample size determination #Simplicity #Speech and Audio Processing #Statistics #Telecommunications #Underwater Acoustics Research
paper · doi:10.1109/29.57542
published in IEEE Transactions on Acoustics Speech and Signal Processing 38(7), 1132-1143 (Institute of Electrical and Electronics Engineers)
openalex publication_date 1990/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Five methods of direction-of-arrival (DOA) estimation which can be derived from the maximum-likelihood (ML) principle are considered. The ML method (MLM) results from the application of the ML principle to the statistics of the observed raw data. The standard multiple signal classification (MUSIC) procedure, called MUSIC-1, is obtained as a brute-force approximation of the MLM. An improved MUSIC procedure, named MUSIC-2, is obtained by applying the ML principle to the statistics of certain linear combinations of the sample noise space eigenvectors. A procedure which compromises between the good performance of the MLM and the computational simplicity of MUSIC is a method of direction estimation (MODE-1) which is derived as a large sample realization of the MLM. A fifth method, called MODE-2, is obtained by using the ML principle on the statistics of certain linear combinations of the sample eigenvectors. MODE-2 is computationally less demanding than the MLM (it is of the same complexity as MODE-1) and statistically more efficient. A numerical comparison of these five DOA estimation methods is presented. It confirms the analytic results on their theoretical performance levels.>