Research On Moving Target Detection Algorithm Of Airborne Radar Based On Sparse Recovery

Space-Time Adaptive Processing(STAP)is a key technology for airborne radar to detect moving targets in heterogeneous clutter environment.Suppression clutter and interference in the detection environment is the basis for the airborne radar to complete the target detection task.For the conventional STAP methods to achieve the desired processing performance,the number of training samples used to estimate the clutter power spectrum cannot be less than twice the degrees of freedom of the system theoretically.However,in practical applications,the clutter environment detected by airborne radar is usually heterogeneous and non-stationary,which is difficult to obtain sufficient uniform training samples,resulting in the severe degradation of STAP performance.Aiming at the problem that it is difficult to obtain statistical information of clutter under few samples,non-uniform and non-stationary conditions,utilizing the sparse distribution of clutter in the angle-Doppler two-dimensional plane,as well as the fast convergence and high resolution of the sparse recovery technique,the moving target detection algorithms based on sparse recovery are studied in this paper.The main research contents are shown as follows.1.A moving target detection method based on fuzzy mathematics and sparse recovery is proposed to address the problem of severe performance loss of STAP due to dense interference.A suitable membership function with the help of the decisionmaking in fuzzy mathematics is constructed,and the covariance matrix by selecting the clutter components that exceed the threshold value in the sparse recovery coefficient vector is estimated in this method.The performance of the STAP filter is better due to the high accuracy of the estimated clutter covariance matrix.The simulation experiments demonstrate that the dense interference in the detection environment are suppressed effectively in the proposed method.2.A moving target detection method based on knowledge aided and sparse Bayesian learning is proposed to overcome the problem that the space-time dictionary after correction still exists grid mismatch.Based on the prior knowledge of clutter,the observed clutter covariance matrix is estimated through training samples,and the prior clutter covariance matrix is corrected.Simulation experiments show that,compared with the sparse Bayesian learning STAP algorithm,the space-time dictionary is corrected effectively and the detection performance of moving target is improved in the proposed algorithm.