Broadband Fusion of Multiband Radar Signals Based on Optimal Dictionary Selection

LU Ruimin; LI Weidong; WANG Rui; ZHANG Fan; LI Muyang; HU Cheng

doi:10.11999/JEIT231309

Volume 46 Issue 5

May 2024

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(5): 2076-2086

LU Ruimin, LI Weidong, WANG Rui, ZHANG Fan, LI Muyang, HU Cheng. Broadband Fusion of Multiband Radar Signals Based on Optimal Dictionary Selection[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2076-2086. doi: 10.11999/JEIT231309

Citation:

LU Ruimin, LI Weidong, WANG Rui, ZHANG Fan, LI Muyang, HU Cheng. Broadband Fusion of Multiband Radar Signals Based on Optimal Dictionary Selection[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2076-2086. doi: 10.11999/JEIT231309

Citation:

PDF( 5121 KB)

Broadband Fusion of Multiband Radar Signals Based on Optimal Dictionary Selection

doi: 10.11999/JEIT231309

LU Ruimin^{1, 3},
LI Weidong^{1, 2, 3
,
,},
WANG Rui^{1, 2, 3},
ZHANG Fan^{1, 3},
LI Muyang^{1, 2, 3},
HU Cheng^{1, 2, 3}

1.
Radar Technology Research Institute, Beijing Institute of Technology, Beijing 100081, China
2.
Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250300, China
3.
Key Laboratory of Real-Time Information Processing Technology of Embedded, Beijing Institute of Technology, Beijing 100081, China

Funds: Shandong Provincial Natural Science Foundation (ZR2022QF073), The National Natural Science Foundation of China (62201049), The Special Fund For Research On National Major Research Instruments (31727901)

Received Date: 2023-11-28
Rev Recd Date: 2024-04-29

Available Online: 2024-05-12

Publish Date: 2024-05-30

Abstract

Abstract

Multiband Fusion is an effective way to broaden bandwidth of radar, which plays a key role in the detection and recognition of small-scale target. However, the existing multiband fusion algorithms still face the problems of slow operation and low precision. Therefore, a super-resolution technique of multiband fusion based on optimal dictionary selection and orthogonal matching pursuit is proposed in this paper. Firstly, the parametric model of multiband radar signal is conducted. Next, Snake Optimizer (SO) is applied to the coherent processing. Then, an Orthogonal Matching Pursuit (OMP) algorithm based on the optimal Geometrical Theory of Diffraction (GTD) dictionary selection is used to extrapolate the vacant spectrum. Experiment results of simulated and measured data are given. Experimental results show that the proposed method can effectively achieve super-resolution. This method combines simplified model rough estimation with complete model fine estimation, effectively reducing the amount of computation and realizing fast and accurate multiband fusion extrapolation processing.
- Multiband fusion,
- Coherence processing,
- Optimal dictionary selection,
- Snake Optimizer (SO),
- Orthogonal Matching Pursuit (OMP)

FullText(HTML)

References(27)

References

[1]	LI Weidong, HU Cheng, WANG Rui, et al. Comprehensive analysis of polarimetric radar cross-section parameters for insect body width and length estimation[J]. Science China Information Sciences, 2021, 64(2): 122302. doi: 10.1007/s11432-020-3010-6.
[2]	蔡炯, 王锐, 胡程. 基于最小熵的机动目标雷达检测[J]. 信号处理, 2022, 38(7): 1416–1423. doi: 10.16798/j.issn.1003-0530.2022.07.007. CAI Jiong, WANG Rui, and HU Cheng. Maneuvering targets radar detection based on minimum entropy[J]. Journal of Signal Processing, 2022, 38(7): 1416–1423. doi: 10.16798/j.issn.1003-0530.2022.07.007.
[3]	王锐, 李卫东, 胡程, 等. 全极化昆虫雷达生物参数反演方法与外场定量试验验证[J]. 信号处理, 2021, 37(2): 199–208. doi: 10.16798/j.issn.1003-0530.2021.02.005. WANG Rui, LI Weidong, HU Cheng, et al. Insect biological parameters estimation method and field quantitative experiment verification for fully polarimetric entomological radar[J]. Journal of Signal Processing, 2021, 37(2): 199–208. doi: 10.16798/j.issn.1003-0530.2021.02.005.
[4]	TIAN Weiming, FANG Linlin, WANG Rui, et al. A robust tracking method focusing on target fluctuation and maneuver characteristics[J]. Science China Information Sciences, 2022, 65(11): 212302. doi: 10.1007/s11432-021-3438-7.
[5]	梁文哲, 冯阳凯, 王锐, 等. 低信噪比下基于YOLOv3的昆虫目标检测[J]. 信号处理, 2022, 38(1): 109–117. doi: 10.16798/j.issn.1003-0530.2022.01.013. LIANG Wenzhe, FENG Yangkai, WANG Rui, et al. Insect target detection based on YOLOv3 under low SNR[J]. Journal of Signal Processing, 2022, 38(1): 109–117. doi: 10.16798/j.issn.1003-0530.2022.01.013.
[6]	HU Cheng, ZHANG Fan, LI Weidong, et al. Estimating insect body size from radar observations using feature selection and machine learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5120511. doi: 10.1109/TGRS.2022.3224618.
[7]	王成. 雷达信号层融合成像技术研究[D]. [博士论文], 国防科学技术大学, 2006. WANG Cheng. A comprehensive study on tombs with single patio in Chang’an area of tang dynasty[D]. [Ph. D. dissertation], National University of Defense Technology, 2006.
[8]	刘承兰, 贺峰, 魏玺章, 等. 基于数据相关的多雷达融合成像相干配准研究[J]. 系统工程与电子技术, 2010, 32(6): 1266–1271. doi: 10.3969/j.issn.1001-506X.2010.06.033. LIU Chenglan, HE Feng, WEI Xizhang, et al. Research on multiple radar fusion imaging coherence compensation based on data correlation[J]. Systems Engineering and Electronics, 2010, 32(6): 1266–1271. doi: 10.3969/j.issn.1001-506X.2010.06.033.
[9]	WANG Tingjing, ZHANG Ying, ZHAO Hua, et al. Multiband radar signal coherent processing algorithm for motion target[J]. International Journal of Antennas and Propagation, 2017, 2017: 4060789. doi: 10.1155/2017/4060789.
[10]	李涛. 基于频带合成的距离分辨率提高方法研究[D]. [硕士论文], 西安电子科技大学, 2020. LI Tao. Research on method of improving distance resolution based on band fusion[D]. [Master dissertation], Xidian University, 2020.
[11]	TIAN Biao, CHEN Zengping, and XU Shiyou. Sparse subband fusion imaging based on parameter estimation of geometrical theory of diffraction model[J]. IET Radar, Sonar & Navigation, 2014, 8(4): 318–326. doi: 10.1049/iet-rsn.2013.0192.
[12]	HURST M P and MITTRA R. Scattering center analysis via Prony's method[J]. IEEE Transactions on Antennas and Propagation, 1987, 35(8): 986–988. doi: 10.1109/TAP.1987.1144210.
[13]	CARRIERE R and MOSES R L. High resolution radar target modeling using a modified Prony estimator[J]. IEEE Transactions on Antennas and Propagation, 1992, 40(1): 13–18. doi: 10.1109/8.123348.
[14]	CUOMO K M, PION J E, and MAYHAN J T. Ultrawide-band coherent processing[J]. IEEE Transactions on Antennas and Propagation, 1999, 47(6): 1094–1107. doi: 10.1109/8.777137.
[15]	ROY R and KAILATH T. ESPRIT-estimation of signal parameters via rotational invariance techniques[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(7): 984–995. doi: 10.1109/29.32276.
[16]	POTTER L C, CHIANG D M, CARRIERE R, et al. A GTD-based parametric model for radar scattering[J]. IEEE Transactions on Antennas and Propagation, 1995, 43(10): 1058–1067. doi: 10.1109/8.467641.
[17]	闫华, 张磊, 陆金文, 等. 任意多次散射机理的GTD散射中心模型频率依赖因子表达[J]. 雷达学报, 2021, 10(3): 370–381. doi: 10.12000/JR21005. YAN Hua, ZHANG Lei, LU Jinwen, et al. Frequency-dependent factor expression of GTD scattering center model for the arbitrary multiple scattering mechanism[J]. Journal of Radars, 2021, 10(3): 370–381. doi: 10.12000/JR21005.
[18]	WIPF D P and RAO B D. Sparse Bayesian learning for basis selection[J]. IEEE Transactions on Signal Processing, 2004, 52(8): 2153–2164. doi: 10.1109/TSP.2004.831016.
[19]	ZHU Xiaoxiu, GUO Baofeng, HU Wenhua, et al. Scene segmentation of multi-band ISAR fusion imaging based on MB-PCSBL[J]. IEEE Sensors Journal, 2021, 21(3): 3520–3532. doi: 10.1109/JSEN.2020.3026109.
[20]	CHEN S S, DONOHO D L, and SAUNDERS M A. Atomic decomposition by basis pursuit[J]. SIAM Review, 2001, 43(1): 129–159. doi: 10.1137/S003614450037906X.
[21]	BAI Xueru, WANG Ge, LIU Siqi, et al. High-resolution radar imaging in low SNR environments based on expectation propagation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(2): 1275–1284. doi: 10.1109/TGRS.2020.3004006.
[22]	GHASEMI M, SHEIKHI A, and PISHROW M M. Multiple radar subbands fusion technique based on generalized likelihood ratio test[J]. Physical Communication, 2021, 46: 101331. doi: 10.1016/j.phycom.2021.101331.
[23]	TROPP J A and GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655–4666. doi: 10.1109/TIT.2007.909108.
[24]	朱晓秀, 刘利民, 胡文华, 等. 基于GTD模型的多视角多频带ISAR融合成像[J]. 系统工程与电子技术, 2023, 45(3): 726–735. doi: 10.12305/j.issn.1001-506X.2023.03.13. ZHU Xiaoxiu, LIU Limin, HU Wenhua, et al. Multi-angle and multi-band ISAR fusion imaging based on GTD model[J]. Systems Engineering and Electronics, 2023, 45(3): 726–735. doi: 10.12305/j.issn.1001-506X.2023.03.13.
[25]	HASHIM F A and HUSSIEN A G. Snake Optimizer: A novel meta-heuristic optimization algorithm[J]. Knowledge-Based Systems, 2022, 242: 108320. doi: 10.1016/j.knosys.2022.108320.
[26]	SKOLNIK M I. Radar Handbook[M]. 3rd ed. New York: McGraw-Hill, 2008: chapter 8, page5.
[27]	HU Cheng, YAN Yujia, WANG Rui, et al. High-resolution, multi-frequency and full-polarization radar database of small and group targets in clutter environment[J]. Science China Information Sciences, 2023, 66(12): 227301. doi: 10.1007/s11432-023-3889-7.