空间目标卡尔曼滤波稀疏成像方法

汪玲; 朱栋强; 马凯莉; 肖卓

doi:10.11999/JEIT170319

空间目标卡尔曼滤波稀疏成像方法

doi: 10.11999/JEIT170319

2.
(南京航空航天大学雷达成像与微波光子技术教育部重点实验室南京 210016）

基金项目:

总装实验技术研究项目(2015SY26A0003)，南京航空航天大学研究生创新基地(实验室)开放基金(kfjj20170407)，中央高校基本科研业务费专项资金

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- 被引次数: 11
出版历程
- 收稿日期: 2017-04-11
- 修回日期: 2018-01-19
- 刊出日期: 2018-04-19

Sparse Imaging of Space Targets Using Kalman Filter

1.
(Key Laboratory of Radar Imaging and Microwave Photonics of the Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)
2.
(Key Laboratory of Radar Imaging and Microwave Photonics of the Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Funds:

The Assembly Test Technology Research Project (2015SY26A0003), The Foundation of Graduate Innovation Center in NUAA (kfjj20170407), The Fundamental Research Funds for the Central Universities

摘要

摘要: 鉴于卡尔曼滤波器(KF)具有优良的信号估计性能，将KF与贪婪算法相结合，该文给出稀疏约束下的基于KF的空间目标逆合成孔径雷达(ISAR)成像方法。考虑到有些空间目标尺寸较大或包含大尺寸部件，或成像积累时间较长，会引入越分辨单元走动(MTRC)和方位向2次相位调制，首先对回波进行MTRC校正，然后构建包含2次相位的观测矩阵，通过使图像锐度最大化，估计目标转动角速度，获得聚焦目标图像，并将估计转速用于方位向图像定标。卫星仿真ISAR数据处理验证了上述成像处理方法的有效性。成像效果优于传统距离多普勒(RD)和正交匹配追踪(OMP)方法。
- ISAR成像 /
- 卡尔曼滤波 /
- 稀疏 /
- 定标
Abstract: In view of the excellent signal estimation performance of the Kalman Filter (KF), combining the KF algorithm with the greedy algorithm and an imaging method is presented for Inverse Synthetic Aperture Radar (ISAR) using KF with sparse constraints. Large space targets including the targets having large-size components and long imaging time may introduce the Migration Through Resolution Cell (MTRC) and quadratic phase modulation in the cross-range. The MTRC correction is firstly performed. Then, the observation matrix is constructed by including the quadratic phase term. By maximizing the image sharpness, an estimation of the target angular velocity as well as a well-focused image can be obtained. The estimated angular velocity can be further used for image cross-range scaling. The processing of the simulated satellite ISAR data verifies the effectiveness of the presented imaging processing method. The image quality is superior to the traditional Range Doppler (RD) method and Orthogonal Matching Pursuit (OMP) method.
- ISAR imaging /
- Kalman filter /
- Sparse recovery /
- Scaling

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参考文献(20)

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