Active Target Localization using Low-Rank Matrix Completion and Unimodal Regression
In this paper, we investigate a novel networked colocated MIMO radar approach that relies on sparse sensing and matrix completion, and enables significant reduction of the volume of data required for accurate target detection and estimation. More specifically, the receive antennas sample the target returns via two sparse sensing schemes, and forward the obtained samples to a fusion center. Based on the data from multiple antennas, the fusion center can formulate and solve a low rank matrix completion problem, which allows for the recovery of all information needed for target parameters estimation. Both the cases of uniform linear and general 2D arrays are considered. The effectiveness of the proposed approach is justified both theoretically and through numerical simulations.