A novel sparse recovery‐based space‐time adaptive processing algorithm based on gridless sparse Bayesian learning for non‐sidelooking airborne radar

Non-sidelooking airborne radar encounters significant non-stationary and heterogeneous clutter environments, resulting in a severe shortage of samples. Sparse recovery-based space-time adaptive processing (SR-STAP) methods can achieve good suppression performance with limited Nonetheless, grid-based SR-STAP algorithms encounter off-grid effects non-sidelooking arrays, which severely degrade the performance. In this study, authors propose novel gridless method continuous spatial-temporal domain to address issue effects. Inspired by fact that sparse Bayesian learning (SBL) framework implicitly performs structured covariance matrix estimation, reparameterise its cost function directly estimate block-Toeplitz from measurements manner. Since proposed is non-convex, we utilise majorisation-minimisation-based iterative procedure matrix. Finally, using standard concept semidefinite programming, derive convex implementation SBL for uniformly sampled systems. Extensive simulation experiments demonstrate exceptional target detection algorithm.