Space-time Adaptive Processing Technique Using Sparse Recovery

By combining multi-arrays and multi-pulses in processing, space-time adaptive processing(STAP) can effectively suppress the clutter and get the better performance of moving target detection. The traditional STAP algorithm usually requires two times the independent identically distributed(IID) training samples to estimate the clutter covariance matrix. However, the requirement is usually not satisfied in the heterogeneous clutter environment, so it is very important to study how to reduce the demand of training samples. In this work, the STAP algorithm based on sparse recovery is studied in the background of airborne radar. The main contents are organized as follows.This work firstly reviews the background of STAP algorithm and introduces the difficulties faced in STAP algorithm. Then the current STAP algorithm, the development history and research status of sparse recovery are overviewed. Finally, the advantages and problems of STAP algorithm based on sparse recovery are discussed.The second chapter introduces the theory of compressed sensing and sparse recovery briefly, followed by the core ideas of various sparse recovery algorithms and their application scenarios. Finally, the thesis analyzed the basic principle of the joint sparse recovery problem based on the mathematical model are given.The algorithm based on the sparsity of space-time power spectrum of clutter is studied. On one hand, it analyzes the sparsity of the space-time power spectrum, then analyzes the rationality of approximate the whole clutter subspace with the finite space time steering vector. On the other hand, it studies the clutter suppression performance of algorithm based on the sparsity of space-time power spectrum of clutter on the background of side-looking of uniform linear array. The simulation results show that the method has the advantage of reducing the number of training samples. Finally, it studies the STAP algorithm of joint sparse recovery based on multiple training samples. The simulation results show that the STAP algorithm based on the minimization of the mixed 2,1l norm can significantly improve the recovery accuracy of the time spectrum and get the better performance of clutter suppression. The STAP algorithm based on the improved orthogonal matching pursuit algorithm can significantly improve the operation efficiency in the case of lose some precision.