基于加权l_(1)范数优化的双基地ISAR稀疏成像算法  

Bi-ISAR imaging based on weighted l_(1) norm optimization algorithm

作　　者：薛东方 朱晓秀 胡文华 郭宝锋 曾慧燕 XUE Dongfang;ZHU Xiaoxiu;HU Wenhua;GUO Baofeng;ZENG Huiyan(Shijiazhuang Campus of the Army Engineering University,Shijiazhuang 050003,China)

机构地区：[1]陆军工程大学石家庄校区,河北石家庄050003

出　　处：《系统工程与电子技术》2021年第4期944-953,共10页Systems Engineering and Electronics

基　　金：国家自然科学基金(61601496);河北省自然科学基金(F2019506031)资助课题。

摘　　要：针对低信噪比条件下实现双基地逆合成孔径雷达(inverse synthetic aperture radar,ISAR)稀疏孔径成像时重构质量较差的问题,提出了一种基于加权l_(1)范数优化的高分辨成像算法。首先,假设各像元稀疏非同分布,利用贝叶斯准则和最大后验概率估计将双基地ISAR稀疏孔径成像问题转化为加权l_(1)范数约束问题,建立成像模型;然后,利用柯西-牛顿算法进行加权l_(1)范数约束最优化问题的求解,实现目标图像重构。由于假设各像元独立非同分布,故通过像元加权的方式更好地利用了目标的能量聚集和结构特性,提高了成像质量。最后,仿真实验验证了算法的有效性和优越性。To solve the problem of poor reconstruction quality in bistatic inverse synthetic aperture radar(ISAR)sparse aperture imaging under low signal-to-noise ratio conditions,a high resolution imaging algorithm based on weighted l_(1) norm optimization is proposed.First,assuming that the image pixels are sparsely distributed,the Bayesian criterion and the maximum a posteriori probability estimation are used to transform the bistatic ISAR sparse aperture imaging problem into a weighted l_(1) norm constraint problem,and the imaging model is established.Second,the Cauchy-Newton algorithm is used to solve the weighted l_(1) norm constrained optimization problem and obtain the target image reconstruction.Because the pixels are assumed to be independent and non-uniformly distributed,the energy aggregation and structural characteristics of the target are better utilized in the way of weighting,which improves the imaging quality.Finally,simulation experiments verify the effectiveness and superiority of the algorithm.

关 键 词：双基地逆合成孔径雷达 稀疏孔径 加权l_(1)范数 压缩感知 优化理论 

分 类 号：V243.2[航空宇航科学与技术—飞行器设计] TN957.52[电子电信—信号与信息处理]