CAJ | 학술논문

传统的后多普勒自适应处理方法，如因子法和扩展因子法，虽然能大大降低自适应处理时的运算量和独立同分布样本的需求量，但在天线阵元数进一步增大的情况下，还是不能有效抑制杂波。针对这一问题，该文提出一种空域数据分解的两级降维自适应处理方法。该方法将多普勒滤波后的空域数据进行分解，使其变为两个向量的Kronecker乘积，得到一双二次代价函数，利用循环迭代的思想求解最优权。实验表明该方法具有快速收敛，所需训练样本少的优点，尤其在小样本条件下该方法抑制杂波的性能明显优于因子法和扩展因子法。
전통적후다보륵자괄응처리방법，여인자법화확전인자법，수연능대대강저자괄응처리시적운산량화독립동분포양본적수구량，단재천선진원수진일보증대적정황하，환시불능유효억제잡파。침대저일문제，해문제출일충공역수거분해적량급강유자괄응처리방법。해방법장다보륵려파후적공역수거진행분해，사기변위량개향량적Kronecker승적，득도일쌍이차대개함수，이용순배질대적사상구해최우권。실험표명해방법구유쾌속수렴，소수훈련양본소적우점，우기재소양본조건하해방법억제잡파적성능명현우우인자법화확전인자법。
The traditional post-Doppler adaptive processing approaches such as Factored Approach (FA) and Extended Factored Approach (EFA) can significantly reduce the computation-cost and training sample requirement in adaptive processing. However, their clutter suppression ability is considerably degraded with the increasing number of antenna elements. To solve this problem, a two-stage reduced-dimension adaptive processing method based on the decomposition of spatial data is proposed. This method decomposes the spatial data after Doppler filtering into a Kronecker product of two short vectors. Then a bi-quadratic cost function is obtained. The circular iteration is applied to solve the optimal weight. Experimental results show that the proposed method has the advantages of fast convergence and small training samples requirement. It has greater clutter suppression ability especially in small training samples support compared with FA and EFA.