摘要
传统捷变频成像方法具有高旁瓣、低分辨率的缺点。鉴于捷变频ISAR回波信号的稀疏性,该文基于原始数据的2维压缩感知方案,在贝叶斯原理框架下,用稀疏贝叶斯算法方差成分扩张压缩方法(Ex Co V)实现捷变频ISAR像的重建。贝叶斯框架下的稀疏重构算法考虑了稀疏信号的先验信息以及测量过程中的加性噪声,因而能够更好地重建目标系数。作为一种新的稀疏贝叶斯算法,Ex Co V不同于稀疏贝叶斯学习(SBL)算法中赋予所有的信号元素各自的方差分量参数,Ex Co V方法仅仅赋予有重要意义的信号元素不同的方差分量,并拥有比SBL方法更少的参数,克服了SBL算法参数多时效性差的缺点。仿真结果表明,该方法能克服传统捷变频成像缺点,并能够实现低信噪比条件下的2维高精度成像。
Traditional frequency agility ISAR imaging method suffers from high sidelobe and low resolution. To improve the resolution, by exploiting the sparsity of targets in the received echo, this paper uses the sparse Bayesian algorithm, namely Expansion-Compression Variance-component based method（Ex Co V）, to reconstruct the ISAR image from the original Compressed Sensing（CS） ISAR data. By taking into account of the prior information of the sparse signal and the additive noise encountered in the measurement process, the sparse recover algorithm under the Bayesian framework can reconstruct the scatter coefficient better than the traditional methods. Different from the Sparse Bayesian Learning（SBL） endowing variance-components to all elements, the Ex Co V only endows variance-components to the significant signal elements. This leads to much less parameters and faster implementation of the Ex Co V than the SBL. The simulation results indicate that it can conquer the problem brought by traditional methods and achieve high precision agility ISAR imaging under the low SNR.
出处
《电子与信息学报》
EI
CSCD
北大核心
2015年第1期1-8,共8页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61171133)
国家自然科学青年基金(61101182
61302148)资助课题
