Research On Fast And Robust Space-Time Adaptive Processing Algorithm For Airborne Array Radar

Space-time adaptive processing(STAP)can detect moving targets in the presence of clutter.STAP method can not only effectively suppress clutter signals,but also significantly improve the performance of target detection of airborne array radar.However,traditional STAP method faces some problems such as large requirement for samples and excessive calculation burden.The STAP algorithm based on sparse recovery can estimate relatively accurate clutter covariance matrix by using only a small number of training snapshots,which provides a new way to overcome the problem of lack of independent and identically distributed training samples in non-uniform clutter environment and non-ideal environment.On this basis,fast and robust STAP algorithms are proposed in this paper,the main contents of which are as follows:1.Aiming at the problem that the number of training samples required by traditional STAP algorithm is too tremendous,an improved direct data domain STAP algorithm based on sparse recovery for target detection is proposed.Firstly,the snapshot data of the cell under test and the global space-time steering dictionary are constructed.Then the larger elements are selected in descending order,and the corresponding elements of clutter steering vector and target steering vector are picked out.Put them separately into an all-zero vector of the same length as the coefficient vector.Finally,the power spectrum of clutter and target is estimated respectively.The comparative analysis of simulation experiment proves that the algorithm can separate the clutter and the target under the condition of only one test sample,so it can avoid the nonstationarity introduced by multiple training samples.It can not only detect the target component quickly,but also has good robustness.2.Aiming at the problem that the interfering signals in training snapshot data may cause the cancellation of moving targets,a multi-snapshot joint sparse reconstruction STAP algorithm based on prior knowledge is proposed.The algorithm utilizes the cell under test and a small number of training snapshots adjacent to it,and the coefficient matrix is obtained by joint sparse restoration.Then the sample data with outliers is eliminated by judgment and comparison.Finally,the prior knowledge of the cell under test is used for clutter reconstruction and filtering.The results of the simulated experiments indicate that the algorithm can estimate the clutter covariance matrix accurately,so as to remove the outliers with the same doppler frequency as the target in the training snapshot,and it has good performance in suppressing outliers and detecting moving targets.