Designing Optimal Frequency Offsets for Frequency Diverse Array MIMO Radar

Frequency diverse array (FDA) radars provide a potential solution to target localization along the slant range and azimuth angle due to the range-angle-dependent transmit beampattern caused by the used frequency increments. However, the S-shaped beampattern resulting from the standard FDA leads to multiple candidate location estimates, introducing ambiguity in the target localization. To make full use of the degrees of freedom (DOF) allowed by the frequency increments, we, here, propose an optimal FDA multiple-input multiple-output (MIMO) frequency design scheme based on the Cramér–Rao lower bound (CRLB). The resulting system, here termed the optimal FDA-MIMO (OFDA-MIMO), is formed by optimizing the expected localization estimation accuracy, given the available prior knowledge of potential target locations. We examine two different modeling scenarios, one where the prior information is known up to intervals on the parameters, formulated using the worst-case CRLB (WCRLB), and one where the parameters are known to come from prior distributions, formulated using the corresponding Bayesian CRLB (BCRLB). The used frequency offsets are found as those minimizing the CRLBs, and may be iteratively refined as further information becomes available in a multipulse scenario. Both theoretical analysis and simulation results validate the preferable performance of the proposed system as compared with alternative frequency selection schemes.