  Subspace based techniques for direction of arrival (DOA) estimation need
large amount of snapshots to detect source directions accurately. This poses a
problem in the form of computational burden on practical applications. The
introduction of compressive sensing (CS) to solve this issue has become a norm
in the last decade. In this paper, a novel CS beamformer root-MUSIC algorithm
is presented with a revised optimal measurement matrix bound. With regards to
this algorithm, the effect of signal subspace deviation under low snapshot
scenario (e.g. target tracking) is analysed. The CS beamformer greatly reduces
computational complexity without affecting resolution of the algorithm, works
on par with root-MUSIC under low snapshot scenario and also, gives an option of
non-uniform linear array sensors unlike the case of root-MUSIC algorithm. The
effectiveness of the algorithm is demonstrated with simulations under various
scenarios.
