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2020-10-01
Microwave Staring Correlated Imaging Based on Quasi-Stationary Platform with Motion Measurement Errors
By
Progress In Electromagnetics Research M, Vol. 97, 1-12, 2020
Abstract
Microwave staring correlated imaging (MSCI) is a promising technique for remote sensing due to its ability to achieve high-resolution microwave imaging without the limitation of relative motion between target and radar. In practical applications, unsteady quasi-stationary platforms, such as tethered aerostat, are often used as carriers of MSCI radar. However, these platforms cannot keep ideally stationary during the imaging process. The platform's motion caused by atmospheric effects will cause time-varying inaccuracy of observation positions. Although navigation systems can measure the platform's motion to compensate for the errors of observation positions, the imaging performance of MSCI may still suffer from degradation due to the measurement errors of navigation systems since MSCI is sensitive to model error. This paper focuses on MSCI based on the quasi-stationary platform with motion measurement errors. First, the MSCI model based on the quasi-stationary platform with motion measurement errors is established under the assumption that the translation and the rotation of the platform are uniform during a coherent imaging interval. Then we propose a self-calibration imaging method for MSCI based on the quasi-stationary platform with motion measurement errors. This method iterates over the steps of target reconstruction and motion measurement errors correction until convergent conditions are met. Simulation results show that the proposed method can correct the motion measurement errors and improve imaging performance significantly.
Citation
Zheng Jiang, Bo Yuan, Jianlin Zhang, Yuanyue Guo, and Dongjin Wang, "Microwave Staring Correlated Imaging Based on Quasi-Stationary Platform with Motion Measurement Errors," Progress In Electromagnetics Research M, Vol. 97, 1-12, 2020.
doi:10.2528/PIERM20061802
References

1. He, X., B. Liu, and D. Wang, "A novel approach of high spatial-resolution microwave staring correlated imaging," Proceedings of 2013 Asia-Pacific Conference on Synthetic Aperture Radar, 75-78, Tsukuba, Japan, September 2013.

2. Guo, Y., X. He, and D. Wang, "A novel super-resolution imaging method based on stochastic radiation radar array," Measurement Science and Technology, Vol. 24, No. 7, 31-36, 2013.
doi:10.1088/0957-0233/24/7/074013

3. Li, D., X. Li, Y. Cheng, Y. Qin, and H. Wang, "Radar coincidence imaging: An instantaneous imaging technique with stochastic signals," IEEE Transactions on Geoscience Remote Sensing, Vol. 52, No. 4, 2261-2271, 2014.
doi:10.1109/TGRS.2013.2258929

4. Zhu, S., A. Zhang, Z. Xu, and X. Dong, "Radar coincidence imaging with random microwave source," IEEE Antennas and Wireless Propagation Letters, Vol. 14, 1239-1242, 2015.
doi:10.1109/LAWP.2015.2399977

5. Guo, Y., D. Wang, and C. Tian, "Research on sensing matrix characteristics in microwave staring correlated imaging based on compressed sensing," 2014 IEEE International Conference on Imaging Systems and Techniques (IST), 195-200, IEEE, 2014.
doi:10.1109/IST.2014.6958472

6. Zhang, L., J. Sheng, M. Xing, Z. Qiao, T. Xiong, and Z. Bao, "Wavenumber-domain autofocusing for highly squinted UAV SAR imagery," IEEE Sensors Journal, Vol. 12, No. 5, 1574-1588, 2012.
doi:10.1109/JSEN.2011.2175216

7. Fornado, G., "Trajectory deviations in airborne SAR: Analysis and compensation," IEEE Transactions on Aerospace and Electronic Systems, Vol. 35, No. 3, 997-1009, 1999.
doi:10.1109/7.784069

8. Cao, N., H. Lee, E. Zaugg, R. Shrestha, and H. Yu, "Estimation of residual motion errors in airborne sar interferometry based on time-domain backprojection and multisquint techniques," IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, No. 4, 2397-2407, 2018.
doi:10.1109/TGRS.2017.2779852

9. Yang, J., X. Huang, J. Thompson, T. Jin, and Z. Zhou, "Compressed sensing radar imaging with compensation of observation position error," IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 8, 4608-4620, 2014.
doi:10.1109/TGRS.2013.2283054

10. Zhou, X., H. Wang, Y. Cheng, et al. "Radar coincidence imaging with phase error using bayesian hierarchical prior modeling," Journal of Electronic Imaging, Vol. 25, No. 1, 013018, 2016.
doi:10.1117/1.JEI.25.1.013018

11. Zhou, X., H. Wang, Y. Cheng, and Y. Qin, "Sparse auto-calibration for radar coincidence imaging with gain-phase errors," Sensors, Vol. 15, No. 11, 27611-27624, 2015.
doi:10.3390/s151127611

12. Tian, C., B. Yuan, and D. Wang, "Calibration of gain-phase and synchronization errors for microwave staring correlated imaging with frequency-hopping waveforms," Proceedings of the IEEE Radar Conference, 1328-1333, 2018.

13. Xia, R., Y. Guo, W. Chen, and D. Wang, "Strip-mode microwave staring correlated imaging with self-calibration of gain-phase errors," Sensors, Vol. 19, No. 5, 1079, 2019.
doi:10.3390/s19051079

14. Zhou, X., H. Wang, Y. Cheng, Y. Qin, and H. Chen, "Radar coincidence imaging for off-grid target using frequency hopping waveforms," International Journal of Antennas and Propagation, Vol. 2016, 1-16, 2016.

15. Zhou, X., H. Wang, Y. Cheng, and Y. Qin, "Off-grid radar coincidence imaging based on variational sparse Bayesian learning," Mathematical Problems in Engineering, Vol. 2016, 1782178, 2016.

16. Zhou, X., B. Fan, H. Wang, Y. Cheng, and Y. Qin, "Sparse bayesian perspective for radar coincidence imaging with array position error," IEEE Sensors Journal, Vol. 17, No. 16, 5209-5219, 2017.
doi:10.1109/JSEN.2017.2723611

17. Li, D., X. Li, Y. Cheng, Y. Qin, and H. Wang, "Radar coincidence imaging in the presence of target-motion-induced error," Journal of Electronic Imaging, Vol. 23, No. 2, 023014, 2014.
doi:10.1117/1.JEI.23.2.023014

18. Jiang, Z., Y. Guo, J. Deng, W. Chen, and D. Wang, "Microwave staring correlated imaging based on unsteady aerostat platform," Sensors, Vol. 19, No. 12, 2825, 2019.
doi:10.3390/s19122825