The problem of distributed MIMO radar in range-spread moving target detection under non-homogeneous environment is studied. Due to the fact that the disturbances (clutters and noises) on distributed MIMO radar are non-homogeneous, there is little homogeneous training data to estimate the disturbance covariance matrices. A new knowledge-aided model is proposed to depict the non-homogeneous characteristics of the disturbance in distributed MIMO radar.Specifically, the disturbance covariance matrices corresponding to different Transmit-Receive (Tx-Rx) pairs are modeled to obey the complex inverse Wishart distribution, but have different power levels.Based on the above prior knowledge, a novel Knowledge-Aided Generalized Likelihood Ratio Test (KA-GLRT) range-spread target detector is proposed for distributed MIMO radar without training data. Simulation results show that: compared with conventional covariance matrix-based (with training data) detectors, the proposed KA-GLRT detector has better detection performance in non-homogeneous environment.