Airborne MIMO Radar Clutter Suppression Based On Sparse Recovery

Space-time adaptive processing(STAP)is the most effective means of clutter suppression for airborne radar,and its research has been extended from phased radar to MIMO radar.Existed studies have shown that MIMO radar has obvious advantages in clutter suppression,which can provide more clutter freedom than phased radar.Traditional STAP methods usually require twice the freedom of system of the independent identically distributed(IID)training samples to yield an average performance loss of roughly 3dB.But in practice,it is often difficult to get enough IID training samples,especially for airborne MIMO radar,the demand for training samples increases greatly because of the increase of data dimension in the receiving space.Therefore,it is necessary to further study high performance STAP algorithm for airborne MIMO radars.Based on airborne MIMO radar,this paper explores the sparse nature of clutter and combines the sparse recovery algorithm with STAP,a series of robust STAP methods for reducing the number of IID training samples and suitable for complex environment are carried out.The main contents of this paper are listed as follows:(1)The sparse constraint in STAP of airborne MIMO radar is studied.Based on airborne MIMO radar signal model,the power spectrum sparsity of clutter is analyzed.Through the clutter covariance matrix decomposition and steering vector correlation analysis,the sparse internal mechanism of the clutter space-time spectrum is discussed and points out the relationship between clutter rank and clutter power spectrum sparse degree,which provides a basis for the following researches.(2)The STAP technology based on sparisity of airborne MIMO is studied.In the homogeneous clutter environment,the STAP technique based on joint sparse recovery is studied,and the performance of SCNR has significantly improved compared with the traditional STAP algorithm.Dealing with the heterogeneous clutter environment,two direct data domain clutter suppression methods based on sparse recovery are proposed,which can effectively suppress clutter and interference without the training samples.(3)The off-grid problem in STAP based on sparsity is studied.Off-grid effects the estimation precision of the clutter power spectrum.Based on sparse bayesian framework,three methods are proposed to correct the space-time frequency of the base matrix.The simulation results show that these methods can reduce the influence of inaccuracy of the power spectrum estimation caused by off-grid and improve the performance of clutter suppression.