  This paper investigates the problem of direction-of-arrival (DOA) estimation
using multiple partially-calibrated sparse subarrays. In particular, we present
the Generalized Coarray Multiple Signal Classification (GCA-MUSIC) DOA
estimation algorithm to scenarios with partially-calibrated sparse subarrays.
The proposed GCA-MUSIC algorithm exploits the difference coarray for each
subarray, followed by a specific pseudo-spectrum merging rule that is based on
the intersection of the signal subspaces associated to each subarray. This rule
assumes that there is no a priori knowledge about the cross-covariance between
subarrays. In that way, only the second-order statistics of each subarray are
used to estimate the directions with increased degrees of freedom, i.e., the
estimation procedure preserves the coarray Multiple Signal Classification and
sparse arrays properties to estimate more sources than the number of physical
sensors in each subarray. Numerical simulations show that the proposed
GCA-MUSIC has better performance than other similar strategies.
