An Improved Iterative Reweighted STAP Algorithm for Airborne Radar

In recent years, sparse recovery-based space-time adaptive processing (SR-STAP) technique has exhibited excellent performance with insufficient samples. Sparse Bayesian learning algorithms have received considerable attention for their remarkable and reliable performance. Its implementation in large-scale radar systems is however hindered by the overwhelming computational load slow convergence speed. This paper aims to address these drawbacks proposing an improved iterative reweighted algorithm based on expansion-compression variance-components (ExCoV-IIR-MSBL). Firstly, a modified probabilistic model SR-STAP introduced. Exploiting intrinsic sparsity prior of clutter, we divide coefficients into two parts: significant part nontrivial irrelevant small or zero coefficients. Meanwhile, only assign independent hyperparameters part, while remaining share common hyperparameter. Then generalized maximum likelihood (GML) criterion adopted classify coefficients, ensuring both accuracy efficiency. Hence, parameter space inference will be significantly reduced, efficiency can considerably promoted. Both theoretical analysis numerical experiments validate that proposed achieves superior sample shortage scenarios.