  In this paper, direction-of-arrival (DOA) estimation using non-coherent
processing for partly calibrated arrays composed of multiple subarrays is
considered. The subarrays are assumed to compute locally the sample covariance
matrices of their measurements and communicate them to the processing center. A
sufficient condition for the unique identifiability of the sources in the
aforementioned non-coherent processing scheme is presented. We prove that,
under mild conditions, with the non-coherent system of subarrays, it is
possible to identify more sources than identifiable by each individual
subarray. This property of non-coherent processing has not been investigated
before. We derive the Maximum Likelihood estimator (MLE) for DOA estimation at
the processing center using the sample covariance matrices received from the
subarrays. Moreover, the Cramer-Rao Bound (CRB) for our measurement model is
derived and is used to assess the presented DOA estimators. The behaviour of
the CRB at high signal-to-noise ratio (SNR) is analyzed. In contrast to
coherent processing, we prove that the CRB approaches zero at high SNR only if
at least one subarray can identify the sources individually.
