This paper deals with the problem of detecting a distributed target in interference and noise. The target signal and interference are assumed to lie in two linearly independent subspaces, and their coordinates are unknown. The noise is Gaussian distributed, with an unknown covariance matrix. To estimate the covariance matrix, a set of training data is supposed available. We derive the Rao test and its two-step variant both in homogeneous and partially homogeneous environments. All of the proposed detectors exhibit a desirable constant false alarm rate. Numerical examples show that the proposed detectors can provide better detection performance than their natural counterparts in some scenarios.