基于双迭代与二阶锥规划的机载雷达稳健降维STAP方法

Robust Reduced-Dimension STAP Method for Airborne Radar Based on BIA and SOCP

针对机载雷达空时自适应处理(STAP)中期望目标导向矢量的失配问题,提出一种基于双迭代与二阶锥规划的稳健降维STAP方法。该方法首先将权矢量分解为空域与时域2个低维权矢量的Kronecker积;然后,分别限定实际空域、时域导向矢量与假定导向矢量之间的误差范数边界,并通过对各自最差性能进行优化,转化为相应的SOCP形式,进而利用双迭代算法实现了对2个低维权矢量的分别求解;最终,合成全维STAP权矢量。该方法在保证稳健STAP性能的同时,通过双迭代降维处理能够有效降低训练样本数需求与运算复杂度,因此更具有实际应用价值。仿真结果验证了提出方法的有效性。

To solve the problem of the mismatch errors between the actual and presumed signal steering vectors in airborne radar space-time adaptive processing（ STAP）,a robust reduced-dimension STAP method based on bi-iterative algorithm（ BIA） and second-order cone programming（ SOCP） is proposed. Firstly the weight vector is decomposed into the Kronecker product of the spatial and temporal weight vectors. Then by imposing a bound on the errors between the actual spatial and temporal steering vectors and the presumed steering vectors,respectively,the optimization of the worst case performance is executed and transformed into the SOCP formulation. Further the BIA is utilized to resolve the two low dimension weight vectors,and finally the full dimension STAP weight vector is synthesized. The proposed method can significantly decrease the training sample requirement and the computational complexity while maintaining the robust STAP performance,which is more valuable for practical engineering application. Simulation results verify the effectiveness of the proposed method.