Sparse recovery-based space-time adaptive processing with array error self-calibration

A sparse recovery-based space-time adaptive processing approach for airborne array radars with array amplitude/phase error self-calibration is proposed. The amplitude/phase error of the array is considered, and the clutter characteristic, i.e. the clutter space-time spectrum, is estimated as a sparse recovery problem with perturbations in the basis matrix and additive measurement noise. An optimisation problem is formulated to estimate the clutter space-time spectrum and array error parameters simultaneously. An iterative alternating descent sparse recovery algorithm to solve the problem is developed. The numerical results indicate that the algorithm can realise array error self-calibration, and thus can obtain accurate clutter characteristic estimation for better clutter suppression.