合成孔径雷达在导航系统中的应用

综述了合成孔径雷达和干涉式合成孔径雷达的发展、导航系统的发展以及它们两者之间的相互渗透和相互辅助的应用情况。针对全天候、全天时的现代高分辨力微波遥感成像的合成孔径雷达,分析了合成孔径雷达与惯导系统组合的性能和方式,研究了惯导系统对合成孔径雷达不足的弥补方法,为合成孔径雷达技术在导航系统中的应用提供了有用的参考。     

逆合成孔径雷达在驼峰测速系统中的应用

目前使用的驼峰测速雷达一般安装在轨旁,应用多普勒原理对溜放车组的速度进行测量,一部雷达只能测量一个股道的溜放车组速度.现基于逆合成孔径雷达测速原理,提出将雷达侧向安装在溜放线路一侧的上方,并建立了雷达回波数学模型,采用了一种基于自聚焦的溜放车组速度估计算法.仿真结果表明:该算法能够精确地连续测量多股道溜放车组的瞬时速度.     

A feature extraction method for synthetic aperture radar(SAR) automatic target recognition based on maximum interclass distance

Synthetic aperture radar(SAR) automatic target recognition is an important application in SAR.How to extract features has restricted the application of SAR technology seriously.In this paper,a new feature extraction method for SAR automatic target recognition based on maximum interclass distance is proposed,which integrates class and neighborhood information.This method can reinforce discriminative power using maximum interclass distance,so it can improve recognition rate effectively.     

SAR-GMTI investigation in hybrid along- and cross-track baseline InSAR

A joint-pixel clutter suppression method based on slope compensation is proposed in this paper. In order to eliminate the effect of the terrain interferometric phase caused by the cross-track baseline in hybrid baseline InSAR, the local independent identical distribution of the clutter is satisfied by using the slope compensation technique, and thus the clutter can be suppressed successfully by using the orthogonality of the clutter subspace and the noise subspace. This approach utilizes the information contained in the current pixel as well as in its neighbors, showing robustness to the image coregistration errors. Both the simulated data and the real airborne data are used in proving the validity of the presented approach. Supported in part by the National Nature Science Foundation of China (Grant No. 60802074) and the Program for New Century Excellent Talents in University     

融合深度学习和凸优化迭代求解策略的逆合成孔径雷达成像方法

目的 针对基于压缩感知（CS）的逆合成孔径雷达（ISAR）成像方法的成像质量和应用一直受到目标场景稀疏性好坏和迭代重建耗时长限制的问题,提出一种基于交替方向乘子法网络（ADMMN）的ISAR成像方法。方法 根据交替方向乘子法（ADMM）求解稀疏假设下CS ISAR成像模型时采取的分裂变量的策略,将凸优化迭代求解过程映射到一个多级的深度神经网络,构建出ADMMN。ADMMN通过训练学习欠采样的ISAR测量数据与高质量目标图像之间的映射关系,借此实现ISAR欠采样数据成像。结果 实验采用仿真卫星数据和实测飞机数据,两种数据的采样率分别为25%和10%。实验结果表明,相较于典型的CS ISAR正交匹配追踪（OMP）成像方法和贪婪卡尔曼滤波（GKF）成像方法,ADMMN成像方法能够更准确地重建目标区域散射点,在虚警（FA）、漏检（MD）和相对均方根误差（RRMSE）等成像质量评估指标上均有改善。在卫星数据成像实验中,相比于OMP和GKF,ADMMN在RRMSE指标上分别降低了49.8%和26.5%。在飞机数据成像实验中,相比于OMP和GKF,ADMMN在RRMSE指标上分别降低了68.7%和74.9%。此外,在验证ADMMN先验信息依赖性的实验中,分别采用卫星训练数据和飞机训练数据训练好的两种ADMMN,都能够对10%的飞机目标测量数据成像。结论 融合深度学习和凸优化迭代求解策略的ADMMN ISAR成像方法能够使用非常少的数据获得高质量的成像结果,且成像效率高。