This study examines moving target detection for airborne radar in heterogeneous environments. The non-homogeneity of the clutter may lead to a shortage of training samples in practice. In contrast to detectors assuming rich training data, the authors consider a knowledge-aided treatment for training data-limited scenarios by incorporating a priori knowledge of the clutter subspace. The proposed detector is developed within the generalised likelihood ratio test framework, based on the sparsity of the clutter response in the space-time domain. It does not require training samples and allows the usage of uncertain a priori knowledge of the clutter subspace, which may be erroneous. The maximum likelihood estimate of the target amplitude is obtained via oblique projection, which provides more accurate estimates compared with the conventional orthogonal projection. Moreover, they jointly exploit the sparsity of the clutter signal, the block-Toeplitz nature of the clutter covariance matrix, and the a priori knowledge about the clutter subspace, and develop an atomic norm minimisation approach to achieve super-resolution estimation of the clutter response. The simulation results indicate that the proposed detector outperforms several state-of-the-art methods.