  Direction of Arrival (DOA) estimation of multiple narrow-band coherent or
partially coherent sources is a major challenge in array signal processing.
Though many subspace- based algorithms are available in literature, none of
them tackle the problem of resolving coherent sources directly, e.g. without
modifying the sample data covariance matrix. Compressive Sensing (CS) based
sparse recovery algorithms are being applied as a novel technique to this area.
In this paper, we introduce Orthogonal Matching Pursuit (OMP) to the DOA
estimation problem. We demonstrate how a DOA estimation problem can be modelled
for sparse recovery problem and then exploited using OMP to obtain the set of
DOAs. Moreover, this algorithm uses only one snapshot to obtain the results.
The simulation results demonstrate the validity and advantages of using OMP
algorithm over the existing subspace- based algorithms.
