MAXIMUM-LIKELIHOOD METHODS FOR DIRECTION-OF-ARRIVAL ESTIMATION

This paper considers five methods of direction-of-arrival (DOA) estimation, all of which can be derived from the maximum likelihood (ML) principle. 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 in this paper, can be 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. The MUSIC procedures are more computationally attractive than the MLM. However, from a statistical viewpoint they may be very inefficient. 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. Finally, the 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. The computationally and statistically efficient MODE-2 introduced in this paper is considered to be a significant new result in this field. The paper also contains a numerical comparison of these five DOA estimation methods, which confirms our analytic results on their theoretical performance levels. © 1990 IEEE