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| Citation: | ZHANG Yin, ZHANG Ping, TUO Xingyu, et al. Sparse targets angular super-resolution reconstruction method under unknown antenna pattern errors for scanning radar[J]. Journal of Radars, 2024, 13(3): 646–666. doi: 10.12000/JR23208
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Sparse Targets Angular Super-resolution Reconstruction Method under Unknown Antenna Pattern Errors for Scanning Radar
DOI: 10.12000/JR23208
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Abstract
Scanning radar angular super-resolution technology is based on the relationship between the target and antenna pattern, and a deconvolution method is used to obtain angular resolution capabilities beyond the real beam. Most current angular super-resolution methods are based on ideal distortion-free antenna patterns and do not consider pattern changes in the actual process due to the influence of factors such as radar radome, antenna measurement errors, and non-ideal platform motion. In practice, an antenna pattern often has unknown errors, which can result in reduced target resolution and even false target generation. To address this problem, this paper proposes an angular super-resolution imaging method for airborne radar with unknown antenna errors. First, based on the Total Least Square (TLS) criterion, this paper considers the effect of the pattern error matrix and derive the corresponding objective function. Second, this paper employs the iterative reweighted optimization method to solve the objective function by adopting an alternative iteration solution idea. Finally, an adaptive parameter update method is introduced for algorithm hyperparameter selection. The simulation and experimental results demonstrate that the proposed method can achieve super-resolution reconstruction even in the presence of unknown antenna errors, promoting the robustness of the super-resolution algorithm. -
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References
[1] Rigelsford J. Introduction to airborne radar[J]. Sensor Review, 2002, 22(3): 265–266.[2] 张良, 祝欢, 吴涛. 机载预警雷达系统架构发展路径研究[J]. 现代雷达, 2015, 37(12): 11–18. doi: 10.16592/j.cnki.1004-7859.2015.12.003.ZHANG Liang, ZHU Huan, and WU Tao. A study on the evolution way of the system architecture of AEW radar[J]. Modern Radar, 2015, 37(12): 11–18. doi: 10.16592/j.cnki.1004-7859.2015.12.003.[3] CLARKE J, 徐映和, 译. 英国预警雷达的发展概况[J]. 现代雷达, 1987, 9(2): 1–6. doi: 10.16592/j.cnki.1004-7859.1987.02.001.CLARKE J, XU Yinghe, translation. Overview of the development of early warning radar in the UK[J]. Modern Radar, 1987, 9(2): 1–6. doi: 10.16592/j.cnki.1004-7859.1987.02.001.[4] ZHANG Qiping, ZHANG Yin, HUANG Yulin, et al. TV-sparse super-resolution method for radar forward-looking imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(9): 6534–6549. doi: 10.1109/TGRS.2020.2977719.[5] LING Hao. Novel radar techniques and applications[J]. IEEE Antennas and Propagation Magazine, 2018, 60(1): 132–134. doi: 10.1109/MAP.2017.2776153.[6] BEKKADAL F. Novel radar technology and applications[C]. 17th International Conference on Applied Electromagnetics and Communications, Dubrovnik, Croatia, 2003: 6–12. doi: 10.1109/ICECOM.2003.1290942.[7] ZHANG Yongchao, ZHANG Yin, LI Wenchao, et al. Super-resolution surface mapping for scanning radar: Inverse filtering based on the fast iterative adaptive approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(1): 127–144. doi: 10.1109/TGRS.2017.2743263.[8] ZHANG Yongchao, JAKOBSSON A, ZHANG Yin, et al. Wideband sparse reconstruction for scanning radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10): 6055–6068. doi: 10.1109/TGRS.2018.2830100.[9] CHEN Rui, LI Wenchao, LI Kefeng, et al. A super-resolution scheme for multichannel radar forward-looking imaging considering failure channels and motion error[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 3501305. doi: 10.1109/LGRS.2023.3234264.[10] KANG Yao, ZHANG Yin, MAO Deqing, et al. Bayesian azimuth super-resolution of sea-surface target in forward-looking imaging[C]. 2020 IEEE Radar Conference (RadarConf20), Florence, Italy, 2020: 1–5. doi: 10.1109/RadarConf2043947.2020.9266692.[11] CHEN Hongmeng, WANG Zeyu, ZHANG Yingjie, et al. Data-driven airborne Bayesian forward-looking superresolution imaging based on generalized Gaussian distribution[J]. Frontiers in Signal Processing, 2023, 3: 1093203. doi: 10.3389/frsip.2023.1093203.[12] MAO Deqing, ZHANG Yin, ZHANG Yongchao, et al. Super-resolution Doppler beam sharpening method using fast iterative adaptive approach-based spectral estimation[J]. Journal of Applied Remote Sensing, 2018, 12(1): 015020. doi: 10.1117/1.JRS.12.015020.[13] LIU Sijia and PAN Minghai. Research on a forward-looking scanning imaging algorithm for a high-speed radar platform[J]. IET Signal Processing, 2023, 17(6): e12221. doi: 10.1049/sil2.12221.[14] MAO Deqing, YANG Jianyu, TUO Xingyu, et al. Angular superresolution of real aperture radar for target scale measurement using a generalized hybrid regularization approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5109314. doi: 10.1109/TGRS.2023.3315310.[15] TUO Xingyu, MAO Deqing, ZHANG Yin, et al. Radar forward-looking super-resolution imaging using a two-step regularization strategy[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 4218–4231. doi: 10.1109/JSTARS.2023.3270309.[16] YOUNG P. Alternative Recursive Approaches to Time-series Analysis[M]. YOUNG P. Recursive Estimation and Time-Series Analysis: An Introduction. Berlin, Heidelberg: Springer, 1984: 205–230.[17] RICHARDS M A. Iterative noncoherent angular superresolution (radar)[C]. 1988 IEEE National Radar Conference, Ann Arbor, USA, 1988: 100–105. doi: 10.1109/NRC.1988.10940.[18] LI Dongye, HUANG Yulin, and YANG Jianyu. Real beam radar imaging based on adaptive Lucy-Richardson algorithm[C]. 2011 IEEE CIE International Conference on Radar, Chengdu, China, 2011: 1437–1440. doi: 10.1109/CIE-Radar.2011.6159830.[19] TAN Ke, LU Xingyu, YANG Jianchao, et al. A novel Bayesian super-resolution method for radar forward-looking imaging based on Markov random field model[J]. Remote Sensing, 2021, 13(20): 4115. doi: 10.3390/rs13204115.[20] CHEN Hongmeng, LI Yachao, GAO Wenquan, et al. Bayesian forward-looking super-resolution imaging using Doppler deconvolution in expanded beam space for high-speed platform[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5105113. doi: 10.1109/TGRS.2021.3107717.[21] LI Weixin, LI Ming, ZUO Lei et al. A computationally efficient airborne forward-looking super-resolution imaging method based on sparse Bayesian learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5102613. doi: 10.1109/TGRS.2023.3260094.[22] CAPON J. High-resolution frequency-wavenumber spectrum analysis[J]. Proceedings of the IEEE, 1969, 57(8): 1408–1418. doi: 10.1109/PROC.1969.7278.[23] ZHANG Yongchao, LI Wenchao, ZHANG Yin, et al. A fast iterative adaptive approach for scanning radar angular superresolution[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(11): 5336–5345. doi: 10.1109/JSTARS.2015.2449090.[24] LI Yueli, LIU Jianguo, JIANG Xiaoqing, et al. Angular superresolution for signal model in coherent scanning radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2019, 55(6): 3103–3116. doi: 10.1109/TAES.2019.2900133.[25] GAMBARDELLA A and MIGLIACCIO M. On the superresolution of microwave scanning radiometer measurements[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4): 796–800. doi: 10.1109/LGRS.2008.2006285.[26] HUO Weibo, TUO Xingyu, ZHANG Yin, et al. Balanced tikhonov and total variation deconvolution approach for radar forward-looking super-resolution imaging[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 3505805. doi: 10.1109/LGRS.2021.3072389.[27] TANG Junkui, LIU Zheng, RAN Lei, et al. Enhancing forward-looking image resolution: Combining low-rank and sparsity priors[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5100812. doi: 10.1109/TGRS.2023.3237332.[28] KOZAKOFF D J. Analysis of radome-enclosed antennas[J]. Artech House, 1997 (685): 217–227.[29] PERSSON K and GUSTAFSSON M. Reconstruction of equivalent currents using a near-field data transformation-with radome applications[J]. Progress in Electromagnetics Research, 2005, 54: 179–198. doi: 10.2528/PIER04111602.[30] Fierro R D, Golub G H, Hansen P C, et al. Regularization by truncated total least squares[J]. SIAM Journal on Scientific Computing, 1997, 18(4): 1223–1241. doi: 10.1137/S106482759426383.[31] GOLUB G H, HEATH M, and WAHBA G. Generalized cross-validation as a method for choosing a good ridge parameter[J]. Technometrics, 1979, 21(2): 215–223. doi: 10.1080/00401706.1979.10489751.[32] JOHNSTON P R and GULRAJANI R M. Selecting the corner in the L-curve approach to Tikhonov regularization[J]. IEEE Transactions on Biomedical Engineering, 2000, 47(9): 1293–1296. doi: 10.1109/10.867966.[33] ENGL H W. Discrepancy principles for Tikhonov regularization of ill-posed problems leading to optimal convergence rates[J]. Journal of optimization theory and applications, 1987, 52: 209–215. doi: 10.1007/BF00941281.[34] RUDIN L I, OSHER S, and FATEMI E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 1992, 60(1/4): 259–268. doi: 10.1016/0167-2789(92)90242-F. -
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- Figure 1. Geometric structure of airborne scanning radar
- Figure 2. Target distribution condition
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Figure 3. One-dimensional point target processing results under little broaden errors (
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Figure 4. One-dimensional point target processing results under large broden errors (
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Figure 5. One-dimensional point target processing results under little random errors (
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Figure 6. One-dimensional point target processing results under large random errors (
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Figure 7. One-dimensional point target processing results under little broaden errors with random errors (
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Figure 8. One-dimensional point target processing results under large broaden errors with random errors (
$ \gamma $ $\xi $ - Figure 9. Root mean square error curve chart under different signal-to-noise ratios
- Figure 10. Area target scene and target distribution
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Figure 11. Two-dimensional area target processing results under little broaden errors (
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Figure 12. Two-dimensional area target processing results under large broaden errors (
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Figure 13. Two-dimensional area target processing results under little random errors (
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Figure 14. Two-dimensional area target processing results under large random errors (
$\xi $ -
Figure 15. Two-dimensional area target processing results under little broaden errors with random errors (
$ \gamma $ $\xi $ -
Figure 16. Two-dimensional area target processing results under large broaden errors with random errors (
$ \gamma $ $\xi $ - Figure 17. Image entropy curve chart under different signal-to-noise ratios
- Figure 18. Corner reflector laying optical figure
- Figure 19. Experiment data processing results