A fast and gridless STAP algorithm based on mixed‐norm minimisation and the alternating direction method of multipliers

Abstract

Abstract Sparse recovery (SR) based space‐time adaptive processing (STAP) methods have received much attention recently due to their dramatically reduced requirements of training samples. However, most of the existing SR‐STAP algorithms suffer from the off‐grid effect induced by the discretisation of angle‐Doppler plane. To eliminate this effect and improve the performance of clutter suppression, a novel gridless mixed‐norm minimisation (MNM) based STAP algorithm is proposed. Unlike previous grid‐based SR‐STAP methods, the clutter covariance matrix (CCM) is estimated in continuous domain by solving a semidefinite programme (SDP), which is established by utilising the properties of the CCM and the compact formulation of MNM. Furthermore, to reduce the computational burden of solving this SDP, a fast iterative algorithm via the framework of the alternating direction method of multipliers (ADMM) is also derived, where the unknown parameters are iteratively updated with closed‐form expressions. Simulation results show that our proposed algorithm achieves better performance of clutter suppression and target detection with lower computational complexity.