Adaptive Detection of Range-Spread Targets in Homogeneous and Partially Homogeneous Clutter Plus Subspace Interference
Tao Jian, Jia He, Bencai Wang, Yu Liu, Congan Xu, Zikeng Xie
Abstract
Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix. The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates. Relying on the two-step criteria, two adaptive detectors based on Gradient tests are proposed, in homogeneous and partially homogeneous clutter plus subspace interference, respectively. Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level. Numerical results show that, the proposed detectors have better performance than their existing counterparts, especially for mismatches in the signal steering vectors.