Sparse Space-time Adaptive Processing Research Articles

A novel non-convex penalty function-based STAP algorithm for airborne passive radar systems

In conventional sparse Space-Time Adaptive Processing (STAP) algorithms, the l0-norm is often used instead of the l1-norm to relax convexity constraints. The approach presented in this paper can be mathematically reformulated as a linear problem, which can be efficiently solved using various convex optimization techniques. While this method effectively circumvents the NP-Hard complexity associated with the l0-norm, it does come with certain potential challenges. For example, the observation matrix must meet the criteria of the Restricted Isometry Property (RIP) and other stringent conditions. In this research, a novel STAP algorithm is introduced, which is based on a non-convex penalty function. This innovative algorithm substitutes the traditional l0-norm with a custom-designed non-convex penalty function and is applied to the Direct Filter Processor (DFP), solved using the recursive least squares (RLS) algorithm. The resulting algorithm, known as gp-RLS, is demonstrated through Monte Carlo simulations to outperform other l1-based and reduced-rank STAP algorithms in terms of faster convergence, improved signal-to-clutter-plus-noise ratio (SCNR), and enhanced target detection performance.

A Joint Sparse Space-time Adaptive Processing Method

At present, most of the sparse space-time adaptive processing(STAP) methods focus on exploiting the clutter sparsity. In this paper, different from the present sparse STAP methods, both the clutter sparsity and the target sparsity in STAP are considered at the same time, and a novel joint sparse STAP method is proposed. The proposed method imposes a sparse regularization on the clutter and the target to the minimum Capon Spectrum criterion. The processing results of the measured data shows that the output SCNR of the proposed method is 3dB higher than the method in [20] and 2dB higher than the method in [21].

L 1 ‐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.