Likelihood-Based CFAR Detectors for FDA-MIMO Radar Under Signal Mismatch

This paper investigates the degradation of detection performance in FDA–MIMO radar systems caused by signal mismatch under constant-velocity target motion and develops a robust detection strategy to mitigate this effect. Under the effective hypothesis, a stochastic term is introduced into the received radar signal to account for mismatch uncertainty. This term is modeled as a Gaussian random variable whose covariance structure is identical to that of the noise while being scaled by an unknown robustness parameter. Based on the resulting statistical model, three robust detectors are derived using the One-Step Generalized Likelihood Ratio Test (OGLRT), the Two-Step GLRT (TGLRT), and the Gradient test. Simulation results demonstrate that all proposed detectors preserve the Constant False Alarm Rate (CFAR) property under the null hypothesis. Further performance evaluations reveal that, in the absence of signal mismatch, the OGLRT and Gradient detectors provide superior detection performance, whereas under mismatched conditions, all three detectors exhibit improved robustness. These findings provide both theoretical insight and practical guidance for the design and implementation of FDA–MIMO radar systems, contributing to the enhancement and optimization of detection performance in realistic operating environments.