基于三迭代与二阶锥规划的机载MIMO雷达稳健降维STAP方法

针对机载多输入多输出(MIMO)雷达空时自适应处理(STAP)中期望目标导向矢量的失配问题,提出一种基于三迭代(TRIA)与二阶锥规划(SOCP)的稳健降维MIMO-STAP方法。首先将MIMO-STAP权矢量分解为发射、接收、多普勒3个低维权矢量的Kronecker积,然后分别限定实际发射、接收、多普勒导向矢量与假定导向矢量之间的误差范数边界,通过优化最差性能,利用SOCP对各个低维权矢量进行三迭代求解,最后进行权矢量合成。该方法在保证机载MIMO雷达获取稳健STAP性能的同时,通过三迭代降维处理,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。仿真结果验证了算法的有效性。

Robust reduced-dimension STAP method for airborne MIMO radar based on TRIA and SOCP

To solve the problem of the mismatch errors between the actual and presumed signal steering vectors in airborne multiple-input multiple-output (MIMO) radar space-time adaptive processing (STAP), a robust reduced-dimension MIMO-STAP method based on tri-iterative algorithm (TRIA) and second-order cone programming (SOCP) is proposed. Firstly the MIMO-STAP weight vector is decomposed into the Kronecker product of the transmitting, the receiving and the Doppler weight vectors. Then by imposing a bound on the errors between the actual transmitting, receiving and Doppler steering vectors and the presumed steering vectors, SOCP is used to optimize the worst case performance. The three low-dimension weight vectors are resolved based on the TRIA to synthesize the final weight vector. The proposed method can significantly decrease the training sample requirement and the computational complexity while maintaining the robust STAP performance of airborne MIMO radar, which is more valuable for practical engineering application. Simulation results verify the effectiveness of the proposed method.