基于l_(p)范数的离格稀疏空时自适应处理算法  

l_(p) Norm Based Off-grid Sparse Space Time Adaptive Processing

作　　者：李志军 向建军[1] 彭芳[1] 刘丹[3] LI Zhijun;XIANG Jianjun;PENG Fang;LIU Dan(Aviation Engineer School,Airforce Engineering University,Xi’an,Shaanxi 710038,China;95247 Unit of PLA,Huizhou,Guangdong 516000,China;93046 Unit of PLA,Shenyang,Liaoning 110000,China)

机构地区：[1]空军工程大学航空工程学院,陕西西安710038 [2]中国人民解放军95247部队,广东惠州516000 [3]中国人民解放军93046部队,辽宁沈阳110000

出　　处：《信号处理》2022年第4期779-787,共9页Journal of Signal Processing

基　　金：空军工程大学校长基金(XZJ2020099)。

摘　　要：稀疏恢复(Sparse Recovery,SR)空时自适应信号处理(Space Time Adaptive Processing,STAP)仅需要少量的杂波样本即可有效抑制杂波,但是稀疏恢复空时自适应信号处理依赖于空时字典,当载机运动方向与天线放置方向存在偏航角时,杂波脊偏离空时字典格点,出现离格问题,从而导致杂波抑制性能下降。已有的基于l_(1)范数类的离格稀疏恢复算法在存在噪声时性能下降,没有充分利用杂波的稀疏性,文章提出一种基于l_(p)(0<p<1)范数的离格空时自适应处理算法,首先将建立基于空时字典更新的稀疏恢复空时自适应模型,然后将该模型松弛为l_(p)(0<p<1)范数的非凸优化问题,最后利用主函数最大化算法将该优化问题转化成凸优化问题,利用两层迭代求解的方法得到该问题的解,最后利用模型的解估计杂波协方差矩阵。通过仿真实验表明,提出的算法能够提高存在离格问题时的杂波恢复精度,抑制杂波的性能也优于已有的基于变分推断的算法。Sparse recovery(SR)space time adaptive processing(STAP)could suppress clutter using a small number of clutter samples,but it highly relies on the space time dictionary.When there was a yaw angel between the direction of the aircraft crab and the antenna placement,the clutter would deviate from the dictionary grids,what we called off-grid.It would cause the degradation of clutter suppression.In this paper,as the reason of some sparse recovery off-grid algorithms don’t performance well when there exists noise and they don’t use the sparsity of clutter well.We proposed a l_(p) norm based STAP algorithm.Firstly,a dynamic dictionary SR STAP model is built.Then the model is relaxed to a l_(p) norm nonconvex problem.Finally,the problem is transformed to a weighted l1 norm problem,and a two-step iterative method is proposed to find the solution.Numerical results show proposed algorithm can recover the clutter accurately,and has better performance in clutter suppression than the variational inference based algorithm.

关 键 词：空时自适应处理 稀疏恢复 离格模型 动态字典 l_(p)范数 

分 类 号：TN957.51[电子电信—信号与信息处理]