Adaptive Detection of Distributed Target for FDA-MIMO Radar in Compound-Gaussian Clutter

To detect distributed target in compound-Gaussian (CG) clutter environments for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. Breaking from conventional Gaussian clutter assumptions, we model sea clutter through a Gamma-Gaussian decomposition, where non-stationary texture follows Gamma distribution and speckle components obey complex Gaussian distribution. The proposed signal model harnesses FDA-MIMO's super-resolution capability to resolve distributed targets across multiple sub-range cells which is a critical advancement beyond traditional point-target models. Based on this model, three distributed target detectors are developed using the two-step generalized likelihood ratio test (GLRT), two-step Rao criterion, and two-step Wald criterion. For covariance matrix estimation of the speckle component, employ either the maximum likelihood (ML) estimator or the maximum a posteriori (MAP) estimator, depending on whether the prior information is utilized. Theoretical analysis demonstrates that the proposed detectors exhibit the constant false alarm rate (CFAR) property, a conclusion further validated through comprehensive simulations. Notably, simulation results show that FDA-MIMO radar outperforms traditional MIMO radar in detection performance in CG clutter, even in challenging scenarios with mainlobe jamming.