Space-time radar problem is well suited to the application of techniques that take advantage of the low-rank property of the space-time covariance matrix. We present a robust solution for data reduction in array processing. The purpose is to improve the false alarm rate robustness and the detector performance. The proposed detector is analyzed and optimized on the assumption that clutter covariance is not known and a random signal has low-rank property. The low-dimensional subspace-based approach leads to a robust false alarm rate detector. The detection performance loss and the false alarm stability loss to unknown clutter covariance have been illustrated by numerical examples. It have been shown that the proposed detector exhibits a quite acceptable performance loss and false alarm stability loss with respect to optimum Neyman-Pearson detector.