A novel orthogonal matching pursuit algorithm based on reduced-dimension dictionary for airborne MIMO radar

In traditional airborne multiple-input multiple-output (MIMO) radar, high correlation of dictionary atoms usually degrades the performance of orthogonal matching pursuit (OMP) algorithm in sparse recovery space-time adaptive processing (SR-STAP). An OMP algorithm based on reduced-dimension dictionary is developed to solve this problem. It divides the dictionary along the clutter ridge and vertical direction of ridge, and eliminates atoms with high correlation by the prior knowledge. The experimental results indicate that the proposed method fully covers the clutter ridge, thus, the performance of clutter spectrum and signal-to-interference-plus-noise-ratio (SINR) are improved under these limitations of high correlation.