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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。
Aiming at the mismatch of desired target steering vectors in space-time adaptive processing (STAP) for airborne multiple input multiple output (MIMO) radar, a robust dimensionality reduction based on triple iterative (TRIA) and second-order cone programming MIMO-STAP method. Firstly, the MIMO-STAP weight vector is decomposed into Kronecker products of the three low-weight vectors of transmitting, receiving and Doppler, and then the error norm boundaries between the actual transmitting, receiving, Doppler steering vector and the assumed steering vector are respectively defined. By optimizing the worst performance, SOCP is used to solve the low-weight vectors of all three iterations, and finally the weight vector is synthesized. The proposed method can reduce the number of training samples and the computational complexity through the triple iterative dimensionality reduction while ensuring the robust STAP performance of airborne MIMO radar. Therefore, this method is more practical. Simulation results verify the effectiveness of the algorithm.