Robust STAP Based on Magnitude and Phase Constrained Iterative Optimization
Shengqi Zhu, Guisheng Liao, Jingwei Xu, Lei Huang, Hing Cheung So · IEEE Sensors Journal · 2019
In this paper, a new approach to space-time adaptive processing (STAP) is proposed that is robust against different deviations in real application, such as array calibration error, steering vector mismatch and so on. The proposed method aims at designing spatial-temporal separable filter by using the magnitude and phase constrained iterative optimization. Applying multiple magnitude and phase constraints on the uncertainty set, the main-beam of the two-dimensional (2-D) frequency response of STAP can be maintained, thus effectively circumventing the performance loss due to the steering vector mismatch. Numerical results demonstrate that, by introducing the magnitude and phase constraints for STAP, the proposed robust 2-D beamformer considerably outperforms the conventional linearly constrained minimum variance (LCMV) algorithm in terms of robustness.