Abstract--Space-time adaptive processing (ST AP) algorithms with coprime arrays can provide good clutter suppression po - tential with low cost in airborne radar systems as compared with their uniform linear arrays counterparts. However, th e performance of these algorithms is limited by the training samples support in practical applications. T o address this issue, a robust two-stage reduced-dimension (RD) sparsity-aware S T AP algorithm is proposed in this work. In the first stage, an RD virtual snapshot is constructed using all spatial channels but only m adjacent Doppler channels around the target Doppler frequency to reduce the slow-time dimension of the signal. In the second stage, an RD sparse measurement modeling is formulated based on the constructed RD virtual snapshot, wh ere the sparsity of clutter and the prior knowledge of the clutte r ridge are exploited to formulate an RD overcomplete diction ary. Moreover, an orthogonal matching pursuit (OMP)-like metho d is proposed to recover the clutter subspace. In order to set the stopping parameter of the OMP-like method, a robust clutter rank estimation approach is developed. Compared wi th recently developed sparsity-aware ST AP algorithms, the si ze of the proposed sparse representation dictionary is much smal ler, resulting in low complexity. Simulation results show that t he proposed algorithm is robust to prior knowledge errors and can provide good clutter suppression performance in low sam ple support. Index T erms--Robust space-time adaptive processing, coprime arrays, prior knowledge, reduced-dimension, sparsity-aw are.