Signal-selective direction finding for fully correlated signals

Summary form only given. A recent technique based on maximum likelihood (ML) arguments has been shown to perform quite well in the presence of fully correlated sources and does so in a computationally efficient manner compared to competing techniques such as exhaustive-search ML or vector-space MUSIC. However, it still suffers from a lack of signal-selectivity which can be disadvantageous in some applications, and it requires that the noise be Gaussian and independent and identically distributed from sensor to sensor for the method to be a true maximum-likelihood technique. An algorithm that effectively addresses the above drawbacks by exploiting the known spectral coherence properties of the desired signals as well as their spatial coherence properties has been developed