摘要
基于逆合成孔径雷达(ISAR)信号的稀疏性,提出了一种基于混合范数稀疏约束的ISAR高分辨成像算法。该方法通过利用压缩感知理论建立了一个基于l2,0混合范数稀疏约束下的最优化ISAR信号模型,通过求解该最优化模型实现短相干积累时间下ISAR图像的高分辨重建。该模型利用了l2,0混合范数的优势,运算时可实现更快收敛,大大提高了模型求解的运算速度;同时,该最优化模型在求解时采用了共轭梯度下降法和快速傅里叶变换操作,提高了算法的求解运算效率。仿真和实测数据都验证了方法的有效性。
Based on the sparsity of inverse synthetic aperture radar(ISAR)signal,an ISAR high-resolution imaging algorithm based on mixed l2,0 norm sparse constraint is proposed in this paper.In this method,an optimal ISAR signal model based on mixed norm sparse constraint is established by using compressed sensing theory,and the high-resolution reconstruction of ISAR image in short coherent accumulation time is realized by solving the optimal model.The model makes use of the advantages of l2,0 mixed norm,which can achieve faster convergence and improve the operation speed.At the same time,the conjugate gradient descent method and fast Fourier transform are used to solve the optimization model,which improves the operation efficiency of the algorithm.Simulation data and measured data verify the effectiveness of the proposed method.
作者
宋代悦
陈倩倩
李晨琦
SONG Daiyue;CHEN Qianqian;LI Chenqi(College of Electronic Information,Qingdao University,Qingdao Shandong 266071,China)
出处
《现代雷达》
CSCD
北大核心
2023年第4期60-65,共6页
Modern Radar
关键词
逆合成孔径雷达
稀疏约束
l2
0混合范数
inverse synthetic aperture radar
sparse constraint
l2,0 mixed norm
