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.