Distributed target detection based on gradient test in deterministic subspace interference

This paper investigates the problem of distributed target detection in the presence of interference and Gaussian noise, where the target signal and interference are assumed to lie in different deterministic subspaces. Building upon this assumption, we propose several adaptive detectors resorting to the gradient criterion tailored for homogeneous environment and partially homogeneous environment. Simulation results indicate that the proposed gradient-based detectors outperform their competitors in some scenarios. Furthermore, all of these Gradient-based detectors exhibit the constant false alarm rate (CFAR) property.