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Volume 40 Issue 4
Apr.  2018
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WANG Ling, ZHU Dongqiang, MA Kaili, XIAO Zhuo. Sparse Imaging of Space Targets Using Kalman Filter[J]. Journal of Electronics & Information Technology, 2018, 40(4): 846-852. doi: 10.11999/JEIT170319
Citation: WANG Ling, ZHU Dongqiang, MA Kaili, XIAO Zhuo. Sparse Imaging of Space Targets Using Kalman Filter[J]. Journal of Electronics & Information Technology, 2018, 40(4): 846-852. doi: 10.11999/JEIT170319

Sparse Imaging of Space Targets Using Kalman Filter

doi: 10.11999/JEIT170319
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

  • Received Date: 2017-04-11
  • Rev Recd Date: 2018-01-19
  • Publish Date: 2018-04-19
  • 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.
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  • PRICKETT M J and CHEN C C. Principles of inverse synthetic aperture radar ISAR imaging[C]. EASCON,80; Electronics and Aerospace Systems Conference, New York, USA, 1980: 340-345.
    LI G, HOU Q, XU S, et al. Multi-target simultaneous ISAR imaging based on compressed sensing[J]. EURASIP Journal on Advances in Signal Processing, 2016, (1): 1-11. doi: 10.1186/s13634-016-0327-1.
    REN X, QIAO L, QIN Y, et al. Sparse regularization based imaging method for inverse synthetic aperture radar[C]. Progress in Electromagnetics Research Symposium, Guangzhou, China, 2016: 4348-4351. doi: 10.1109/PIERS. 2016.7735622.
    HU X, TONG N, DING S, et al. ISAR imaging with sparse stepped frequency waveforms via matrix completion[J]. Remote Sensing Letters, 2016, 7(9): 847-854. doi: 10.1080/ 2150704X.2016.1192699.
    苏伍各, 王宏强, 邓彬, 等. 基于方差成分扩张压缩的稀疏贝叶斯ISAR成像方法[J]. 电子与信息学报, 2014, 36(7): 1525-1531. doi: 10.3724/SP.J.1146.2013.01338.
    SU Wuge, WANG Hongqiang, DENG Bin, et al. Sparse bayesian representation of the ISAR imaging method based on ExCoV[J]. Journal of Electronics Information Technology, 2014, 36(7): 1525-1531. doi: 10.3724/SP.J.1146. 2013.01338.
    RAO W, LI G, WANG X, et al. Parametric sparse representation method for ISAR imaging of rotating targets [J]. IEEE Transactions on Aerospace Electronic Systems, 2014, 50(2): 910-919. doi: 10.1109/TAES.2014.120535.
    TOMEI S, BACCI A, GIUSTI E, et al. Compressive sensing- based inverse synthetic radar imaging from incomplete data[J]. IET Radar, Sonar Navigation, 2016, 10(2): 386-397. doi: 10.1049/iet-rsn.2015.0290.
    ZHANG L, XING M D, and QIU C W. Resolution enhancement for ISAR imaging under low SNR via improved statistical compressive sensing[J]. IEEE Transactions on Geoscience Remote Sensing, 2010, 48 (10): 3824-3838. doi: 10.1109/TGRS.2010.2048575.
    BACCI A, GIUSTI E, CATALDO D, et al. ISAR resolution enhancement via compressive sensing: A comparison with state of the art SR techniques[C]. International Workshop on Compressed Sensing Theory and ITS Applications to Radar, Sonar and Remote Sensing. Aachen, Germany, 2016: 227-231. doi: 10.1109/CoSeRa.2016.7745734.
    吴敏, 邢孟道, 张磊. 基于压缩感知的二维联合超分辨ISAR成像算法[J]. 电子与信息学报, 2014, 36(1): 187-193. doi: 10.3724/SP.J.1146.2012.01597
    WU Min, XING Mengdao, and ZHANG Lei. Two dimensional joint super-resolution ISAR imaging algorithm based on compressive sensing[J]. Journal of Electronics Information Technology, 2014, 36(1): 187-193. doi: 10.3724/ SP.J.1146.2012.01597.
    VASWANI N. Kalman filtered compressed sensing[C]. IEEE International Conference on Image Processing, California, USA, 2008: 893-896. doi: 10.1109/ICIP.2008.4711899.
    WANG L and LOFFELD O. ISAR imaging using a null space -1 minimizing Kalman filter approach[C]. International Workshop on Compressed Sensing Theory Its Applications to Radar, Aachen, Germany, 2016: 232-236. doi: 10.1109/ CoSeRa.2016.7745735.
    APRILE A, MAURI A, and PASTINA D. Real time rotational motion compensation algorithm for focusing spot- SAR/ISAR images in case of variable rotation-rate[C]. European Radar Conference, Amsterdam, The Netherlands, 2004: 141-144.
    李源. 逆合成孔径雷达理论与对抗[M]. 北京: 国防工业出版社, 2013: 139-142.
    LI Yuan. Theory and Countermeasure of Inverse Synthetic Aperture Radar[M]. Beijing: National Defence Industry Press, 2013: 139-142.
    BACCI A, GIUSTI E, CATALDO D, et al. ISAR resolution enhancement via compressive sensing: A comparison with state of the art SR techniques[C]. International Workshop on Compressed Sensing Theory Its Applications to Radar, Aachen, Germany, 2016: 227-231. doi: 10.1109/CoSeRa.2016. 7745734.
    汪玲. ISAR运动补偿技术研究[D]. [硕士论文], 南京航空航天大学, 2003: 28-35.
    WANG Ling. Study on ISAR motion compensation[D]. [Master dissertation], Nanjing University of Aeronautics and Astronautics, 2003: 28-35.
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