空域数据重排的后多普勒自适应处理方法

传统的后多普勒自适应处理方法,如因子法(FA)和扩展因子法(EFA)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,但由于实际中均匀训练样本数目的限制,当天线阵元数进一步增大时,FA和EFA抑制杂波和检测动目标的能力会显著恶化。针对这一问题,提出了一种空域数据重排的后多普勒自适应处理方法。该方法将多普勒滤波后的空域数据重排为一行列数相近的矩阵,空域滤波器权系数也表示成可分离的形式,从而得到一双二次代价函数,利用循环迭代的思想求解权系数。实验表明该方法具有快速收敛,所需训练样本少的优点,尤其在大阵列、小样本条件下该方法抑制杂波的性能明显优于FA和EFA。

Post-Doppler adaptive processing method based on spatial data rearrangement

The traditional post-Doppler adaptive beam-forming approaches such as factored approach (FA) and extended factored approach (EFA) can significantly reduce the computation-complexity and training sample requirement in adaptive processing. However, their clutter suppression and moving target detection ability can be notably degraded with the increasing number of antenna elements since the homogeneous training samples in real clutter environment are always limited. Aimed at this problem, the post-Doppler adaptive processing method based on the spatial data rearrangement is proposed. This method rearranges the spatial data vector, after being filtered by Doppler, into a matrix that has close columns and rows. The spatial weights are also re-expressed as a separation form. Then a bi-quadratic cost function is obtained. The cyclic iteration is applied to solving the desired weight vector. Experimental results show that the proposed method has the advantages of fast convergence and small training sample requirement. It also has greater clutter suppression ability especially in large antenna array elements compared to FA and EFA.