Alternating minimisation approach to generalised MUSIC and its performance

The generalised multiple signal classification (GMUSIC) is capable of localising the coherent signals that are incident on a sensor array. The condition under which GMUSIC can accurately resolve a coherent signal group is presented. The author proves that its asymptotic performance is the same as that of the maximum likelihood (ML) estimation using the coherency profile. An algorithm for the GMUSIC estimation is proposed which requires one-dimensional searches based on the alternating minimisation of its cost function. Simulation results show that the performance of the proposed method is very close to the Cramer-Rao bound (CRB) for the coherency profiled ML estimation which is smaller than that of the conventional ML with no use of the coherency profile