小样本机载分布式雷达空时自适应处理方法

针对机载分布式雷达地杂波和干扰抑制的问题，提出一种基于小样本协方差重构的机载分布式雷达空时自适应处理（space-time adaptive processing，STAP）方法。该方法根据分布式雷达接收的杂波与干扰具有相关性不同的特点，首先利用小样本估计出各子雷达的杂波空时协方差矩阵和干扰全空域协方差矩阵，然后通过块对角延拓和时域拓展方法得到完整的空时协方差矩阵。所提方法利用全部空域自由度，在有效抑制杂波的同时抑制较多数量的干扰，克服了理想协方差矩阵估计时所面临的独立同分布（independent and identically distributed，i.i.d.）样本严重不足的问题。理论分析和仿真实验验证了所提方法的有效性。

Small-sample Space-time Adaptive Processing for Airborne Distributed Radars

A space-time adaptive processing (STAP) method based on small-sample covariance reconstruction is proposed for the ground clutter interference problem of airborne distributed radar down sight operation. Based on the characteristics that the received clutter and interference of distributed radar have different correlations; the method first estimates the clutter space-time covariance matrix and the interference full-space covariance matrix of each subadars using small samples and then obtains the complete space-time covariance matrix by block diagonal extension and time-domain expansion methods. The proposed method utilizes all the airspace degrees of freedom to effectively suppress the clutter while suppressing a larger number of interferences, overcoming the problem of independent and identically distributed (i.i.d.) samples that are grossly inadequate to the estimation of an ideal covariance matrix. The effectiveness of the proposed method is verified through theoretical analysis and simulation experiments.