  Space-time adaptive processing (STAP) algorithms with coprime arrays can
provide good clutter suppression potential with low cost in airborne radar
systems as compared with their uniform linear arrays counterparts. However, the
performance of these algorithms is limited by the training samples support in
practical applications. To address this issue, a robust two-stage
reduced-dimension (RD) sparsity-aware STAP 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, where the sparsity of clutter and the prior knowledge of the
clutter ridge are exploited to formulate an RD overcomplete dictionary.
Moreover, an orthogonal matching pursuit (OMP)-like method 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 with recently developed sparsity-aware STAP algorithms, the size of
the proposed sparse representation dictionary is much smaller, resulting in low
complexity. Simulation results show that the proposed algorithm is robust to
prior knowledge errors and can provide good clutter suppression performance in
low sample support.
