A Low-Complexity MIMO Radar STAP Strategy for Efficient Sea Clutter Suppression

Abstract

When sparse reconstruction (SR) space-time adaptive processing (STAP) strategy is employed for sea clutter suppression, the computational burden imposed by the spectrum reconstruction process is still a challenge. Hence, a low-complexity MIMO radar SR STAP strategy is developed via reducing model dimension and optimizing the sparse reconstruction scheme. On one hand, STAP echo model is projected into space and time domains owing to the uncoupled relationship, thus the computation burden involved with large measurement matrix can be lessened by low-dimension structure. Meanwhile, a coprime scheme is exerted to improve the degree of freedom (DOF) in sub models and ensure lower sampling consumption. On the other hand, in order to realize efficient spectral reconstruction, an improved SR algorithm is formulated via developing a prior matrix factor and a tradeoff factor. Convergence speed of the algorithm is promoted by tuning the factors reasonably, clutter spectrum is quickly accessible with less iterations so that adaptive filter can be constructed efficiently. According to the experiment results, high-efficiency spectral reconstruction is realized and effective clutter suppression performance can be ensured.