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

A novel CS Beamformer root-MUSIC algorithm and its subspace deviation analysis