MUSIC and maximum likelihood techniques on two-dimensional DOA estimation with uniform circular array

Abstract

The authors describe the application of multiple signal classification (MUSIC) and maximum likelihood (ML) techniques to the joint azimuthal and elevational directions-of-arrival (AEDOA) estimation with a uniform circular array. Both deterministic and random source signal models are considered. The asymptotic statistical properties of MUSIC and ML estimation error vectors for AEDOA parameters are investigated. In particular, explicit analytical expressions are derived for asymptotic MUSIC and ML covariance matrices as well as Cramer-Rao lower bounds (CRLBs). These analytical formulae are employed in the theoretical performance study. Computer simulation results are presented to validate theoretical predictions and compare the performance of MUSIC and ML methods. It is shown that the performance of unconditional ML is superior to that of deterministic ML, which is, in turn, better than that of MUSIC.