Gridless Sparse Clutter Nulling STAP Based on Particle Swarm Optimization

Sparse clutter nulling space–time adaptive processing (STAP) methods achieve superior performance in the case of limited samples, so they are suitable for nonhomogeneous clutter environments. Traditional sparse clutter nulling STAP algorithms try to estimate the clutter subspace by selecting a suitable set of space–time steering vectors (atoms) in spatial-Doppler profile grids. However, the off-grid effect is inevitable for the nonside-looking case due to the nonlinear distribution of clutter and, thus, leads to significant performance degradation. To solve this problem, a gridless sparse clutter nulling STAP algorithm based on the particle swarm optimization (PSO) named PSO-STAP is proposed in this letter. A criterion function to evaluate the suitability of atoms is first devised. By regarding the criterion as a fitness function, PSO-STAP can select atoms in gridless spatial-Doppler profile, which is more suitable to construct the accurate clutter subspace. Numerical results verified the feasibility and superiority of the proposed algorithm.