  Space-time adaptive processing (STAP) is a well-known technique in detecting
slow-moving targets in the presence of a clutter-spreading environment. When
considering the STAP system deployed with conformal radar array (CFA), the
training data are range-dependent, which results in poor detection performance
of traditional statistical-based algorithms. Current registration-based
compensation (RBC) is implemented based on a sub-snapshot spectrum using
temporal smoothing. In this case, the estimation accuracy of the configuration
parameters and the clutter power distribution is limited. In this paper, the
technique of sparse representation is introduced into the spectral estimation,
and a new compensation method is proposed, namely RBC with sparse
representation (SR-RBC). This method first converts the clutter spectral
estimation into an ill-posed problem with the constraint of sparsity. Then, the
technique of sparse representation, like iterative reweighted least squares
(IRLS), is utilized to solve this problem. Then, the transform matrix is
designed so that the processed training data behaves nearly stationary with the
test cell. Because the configuration parameters and the clutter spectral
response are obtained with full-snapshot using sparse representation, SR-RBC
provides more accurate clutter spectral estimation, and the transformed
training data are more stationary so that better signal-clutter-ratio (SCR)
improvement is expected.
