L1-regularised joint iterative optimisation space-time adaptive processing algorithm

In this study, two novel joint iterative optimisation space-time adaptive processing (STAP) algorithms are proposed based on L-1-regularised for airborne radar. Both of them are implemented in generalised sidelobe canceller (GSC) architecture. When the rank of the interference covariance matrix is far fewer than the system degrees of freedom, the STAP filter weight will be sparse. In this case, a sparse constraint is further imposed to the minimum variance criterion of GSC. To solve this optimisation problem, a joint iterative recursive least squares (RLS) algorithm (L-1-JI-RLS) and a joint iterative least mean square (LMS) algorithm (L-1-JI-LMS), both of which are based on L-1-regularised, are proposed. The L-1-JI-RLS algorithm achieves the minimum output power by adjusting the penalty parameter and filter weight adaptively, while the L-1-JI-LMS algorithm does it by adjusting the penalty parameter, step and filter weight adaptively. The computational complexity of the L-1-JI-LMS algorithm is far lower than the conventional and some sparse STAP algorithms. Monte Carlo experiments validate that both the L-1-JI-RLS and L-1-JI-LMS algorithms outperform other L-1-based and reduced-rank STAP algorithms, such as a faster convergence rate, improved output signal-to-interference-plus-noise-ratio and target detection performance.