Fast Parameter Estimation Algorithms for Conformal FDA-MIMO Radar

In order to avoid the higher computational complexity caused by multidimensional parameter search, we investigate three reduced-dimension parameter estimation algorithms for the conformal frequency diverse array multiple-input–multiple-output (FDA-MIMO) radar, which are named the reduced-dimension multiple signal classification (RD-MUSIC), RD-MUSIC based on subarray (RDMBS), and parameter separation and estimation based on the virtual subarray (PSEBVS), respectively. All proposed methods are verified by numerical results which show that the proposed algorithms can make a balance between complexity and precision, such as the PSEBVS achieves lower complexity with coarse precision, while the RDMBS algorithm benefits better estimation performance at the cost of a little higher complexity. Besides, compared with the 3-D multiple signal classification (3D-MUISC) algorithm, all the proposed algorithms can jointly estimate the angle and range with lower complexity as well as the comparable estimation performance, especially for RD-MUSIC and RDMBS algorithms. Finally, an adaptive selection system for parameter estimation algorithms is presented to satisfy various requirements in different application scenarios.