Research On Knowledge-Aided Space-Time Adaptive Clutter Suppression Algorithm

The target detection is one of the important concern of modern radar systems,among them,the interference signal in radar echo is an important factor affecting target detection.The effects of clutter signals are particularly serious when the radar operates at look-down mode.Therefore,clutter suppression is a key issue for target detection of radar systems.Space-time adaptive processing(STAP)has attracted widespread attention as an effective clutter suppression technique,and how to accurately estimate the clutter covariance matrix(CCM)is the basic problem of clutter suppression in the STAP method.In order to improve the clutter suppression performance of STAP,this thesis focuses on the clutter suppression algorithm based on STAP.The main research contents of this thesis are as follows:(1)Aimed at the problem that the traditional STAP methods require a large training sample and the prior knowledge of the existing knowledge-aided STAP methods(KA-STAP)usually depend on the radar operating environment,which is difficult to obtain it accurately.This thesis proposes a KA-STAP method based on cyclic characteristic,which improves the clutter suppression performance of STAP by estimating CCM more accurately.This thesis first analyzes and proves the cyclic characteristic of CCM.The cyclic CCM is constructed based on this characteristic.Final,on the basis of the CCM estimated by the traditional STAP methods and the existing KA-STAP methods,this thesis proposes to use the cyclic CCM as a prior matrix to obtain a more accurate CCM estimation.The prior knowledge of the proposed method does not depend on the radar system structure and its operating environment,so it can be combined with existing STAP methods and can further improve the clutter suppression performance of the STAP methods.(2)Aimed at the problem that the weight parameter selection of the cyclic prior matrix and the sample covariance matrix in the KA-STAP method based on cyclic characteristic.This thesis gives two kinds of KA-STAP adaptive parameter selection algorithms based on cyclic characteristic.We noticed that the cyclic prior matrix is combined with the sample covariance matrix to obtain the final CCM estimation in the KA-STAP method based on cyclic characteristic,which is often not accurate enough.Therefore,in order to solve the above problem,this thesis starts from the minimummean square error criterion and the matrix eigenvector weight criterion,adaptively selects the cyclic prior matrix and the sample covariance matrix weight parameters to obtain a more accurate CCM estimation.(3)Aimed at the problem that the training samples will be polluted by the target signal,which will lead to errors in the estimated CCM,this thesis gives a knowledge-based clutter covariance matrix reconstruction method.We noticed that the traditional clutter covariance matrix reconstruction method based on the clutter Capon spectrum can not completely eliminate the influence of the target in the CCM.Therefore,in order to solve the above problem,This thesis proposes to perform two-step elimination on the impact of the target signal,that is,the first step is to remove the influence of the target by reconstructing the training sample signal,and the second step is to eliminate the influence of the target signal on the CCM by integrating the clutter Capon spectrum in the non-target region.