A low complexity method for direction-of-arrival (DOA) estimation via array covariance matrix sparse representation is proposed, in which DOA estimation can be cast as the sparse recovery problem of only a single measurement vector when multiple snapshots are available. Based on the Khatri-Rao product, the proposed method shows an extended-aperture and leads to a significant improvement in the resolution limit. Simulation results confirm the efficacy of the proposed method.
As a new type of radar, the FDA-MIMO radar has a good improvement on side lobe suppression and target detection performance compared with the conventional MIMO radar. However, the existing researches on FDA-MIMO radar are almost based on far-field. In this paper, FDA-MIMO radar is applied to the detection of subsurface targets. Aimed at near-subsurface targets, we formulated the signal model of FDA-MIMO radar and combined it with the algorithm of grid of beam (GOB) to detect. Compared with conventional MIMO radar detection, we verified the effectiveness of the proposed method through theoretical simulation.
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