MUSIC AND MAXIMUM-LIKELIHOOD TECHNIQUES ON 2-DIMENSIONAL DOA ESTIMATION WITH UNIFORM CIRCULAR ARRAY

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 unifo rm 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 par ticular, explicit analytical expressions are derived for asymptotic MU SIC and ML covariance matrices as well as Cramer-Rao lower bounds (CRL Bs). These analytical formulae are employed in the theoretical perform ance study. Computer simulation results are presented to validate theo retical predictions and compare the performance of MUSIC and ML method s. It is shown that the performance of unconditional ML is superior to that of deterministic ML, which is, in turn, better than that of MUSI C.