| The performance of airborne radar can be affected by the clutter and jamming,which make it difficult to detect the weak scattering and distant target.Meanwhile,the non-uniform and nonstationary clutter work condition of airborne radar cause the difficulties on obtaining sufficient independent and identically distributed(IID)training samples.This problem,which is called small sample problem,reduces the clutter suppression performance of Space Time Adaptive Processing(STAP).Sparse Recovery STAP is one of the most effective method to solve the small sample problem,and thus it attracts the attention of more and more researchers.It can achieve better clutter(clutter and jamming)suppression performance though the training samples is few.Recently,the Sparse Recovery STAP has made some progress,but it still needs to be improved.Furthermore,most existing work only take the clutter into consideration while ignore the influence of interference.In the case of the coexistence of clutter and interference,the Sparse Recovery STAP method relies on prior information with regard to the jamming,which make the solve scheme complex.Therefore,it is necessary to propose the STAP method for Sparse Recovery.This thesis is based on the airborne radar application background and propose the Sparse Recovery STAP method under the small samples condition.To deal with the high complexity and poor stability of STAP for direct data domain sparsity-based STAP utilizing sub aperture smoothing techniques,a clutter suppression method based on small sample sub aperture smooth sparse restoration is proposed,which can reduce the problem complexity and has more stable and accurate estimation of clutter covariance.Considering that the existing Sparse Recovery STAP method rely on the prior jamming information under the background of clutter and jamming,two clutter and jamming suppression methods based on data statistics are proposed.The main work of this thesis can be summarized as follows:1.A clutter suppression method based on the smooth sparse recovery of small sample sub apertures is proposed.The problem with regard to high complexity and poor stability of the SASM-D3SR-STAP(Direct Data Domain Sparsity-based STAP Utilizing Sub Aperture Smoothing Techniques)method is solved:(1)A regularization factor is introduced and all subsnapshots are joint sparse recovered at the same time,which can reduce the complexity;(2)It is extended to multiple sample implementations,and the sample selection is performed based on the Canberra distance between the unit to be detected and the training sample support set coefficient vector,which can estimate clutter covariance matrix more accurately.Simulation results show that the method can improve the clutter suppression performance effectively.2.Two STAP methods for suppressing clutter and jamming based on the data statistics method is proposed.The first method suppresses clutter and jamming at the same time.Firstly,the SRCN-STAP(Sparse Representation based Clutter Nulling STAP)algorithm is utilized to sparsely restore the coefficients of clutter and jamming.Multiple atoms are selected to the support set during each iteration,which can decrease the number of sparse reconstruction iterations;Next,the support set atoms are selected again according to the space-time twodimensional coupling characteristic curve of the clutter and more accurate clutter atoms can be obtained;Then the jamming direction can be given by the statistical sparse coefficient of the rest atoms;Finally,space-time covariance matrix and adaptive filter are constructed to suppress clutter and jamming.The second method can be divided into two steps,which adopts the cascaded suppression of jamming and clutter.Based on the jamming atoms obtained by the first method,a spatial projection matrix is constructed to suppress jamming.Finally,STAP is utilized to suppress clutter.Simulation results show that both the two methods can improve the suppression performance of clutter and jamming effectively. |
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