Scattering center modeling for low-detectable targets

doi:10.23919/JSEE.2022.000051

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (3): 511-521.doi: 10.23919/JSEE.2022.000051

• ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

Scattering center modeling for low-detectable targets

Yanxi CHEN(), Kunyi GUO*(), Guangliang XIAO(), Xinqing SHENG()

¹ Institute of Radio Frequency Technology and Software, School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China

Received:2020-12-22 Online:2022-06-18 Published:2022-06-24
Contact: Kunyi GUO E-mail:cyx_bit@126.com;guokunyi@bit.edu.cn;20111462@bit.edu.cn;xsheng@bit.edu.cn
About author:|CHEN Yanxi was born in 1994. He received his B.S. degree from Beijing Institute of Technology, Beijing, China, in 2017. He is currently a Ph.D. degree candidate in Beijing Institute of Technology. His major research interests include radar scattering center and electromagnetic simulation and application. E-mail: cyx_bit@126.com||GUO Kunyi was born in 1976. She received her Ph.D. degree from Chinese Academy of Sciences, Beijing, China, in 2005. She is currently a professor with the School of Information and Electronics, Beijing Institute of Technology, Beijing. Her current research interests include electromagnetic scattering, antenna propagation, scattering center modeling, and radar signal processing. E-mail: guokunyi@bit.edu.cn||XIAO Guangliang was born in 1992. He received his B.S. degree from Beijing Institute of Technology in 2016. He is currently a Ph.D. degree candidate in Beijing Institute of Technology. His major research interests include radar target character and electromagnetic simulation and application. E-mail: 20111462@bit.edu.cn||SHENG Xinqing was born in 1968. He received his B.S., M.S., and Ph.D. degrees from University of Science and Technology of China, Hefei, China, in 1991, 1994, and 1996, respectively. He is currently a chair professor of the School of Information and Electronic, Beijing Institute of Technology, Beijing, China. His current research interests include computational electromagnetics, target electromagnetic characteristics and stealth design, and complex electromagnetic environment simulation. E-mail: xsheng@bit.edu.cn
Supported by:
This work was supported by the National Key R&D Program of China (2017YFB0202500), and the National Natural Science Foundation of China (61771052).

Abstract

Abstract:

The scattering centers (SCs) of low-detectable targets (LDTs) have a low scattering intensity. It is difficult to build the SC model of an LDT using the existing methods because these methods mainly concern dominant SCs with strong scattering contributions. This paper presents an SC modeling approach to acquire the weak SCs of LDTs. We employ the induced currents at the LDT to search SCs, and the joint time-frequency transform together with the Hough transform to separate the scattering contributions of different SCs. Particle swarm optimization (PSO) is applied to improve the estimation results of SCs. The accuracy of the SC model built by this approach is verified by a full-wave numerical method. The validation results show that the SC model of the LDT can precisely simulate the signatures of high-resolution images, such as high-resolution range profile and inverse synthetic aperture radar (ISAR) images.

Key words: low-detectable target (LDT), stealth target, scattering center (SC) model, time-frequency representation, full-wave method

Yanxi CHEN, Kunyi GUO, Guangliang XIAO, Xinqing SHENG. Scattering center modeling for low-detectable targets[J]. Journal of Systems Engineering and Electronics, 2022, 33(3): 511-521.

Figures/Tables 20

Fig 1

Fig 2

Fig 3

Fig 4

Fig 5

Fig 6

Fig 7

Fig 8

Fig 9

Fig 10

Fig 11

Fig 12

Fig 13

Table 1

Table 2

Fig 14

Fig 15

Fig 16

References 32

1	Editorial board of special issue for “computational electromagnetics” Ten problems in computational electromagnetics. Chinese Journal of Radio Science, 2020, 35 (1): 3- 12.
2	DING B Y, WEN G J, HUANG X H, et al Target recognition in synthetic aperture radar images via matching of attributed scattering centers. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10 (7): 3334- 3347. doi: 10.1109/JSTARS.2017.2671919
3	LI T L, DU L Target discrimination for SAR ATR based on scattering center feature and k-center one-class classification . IEEE Sensors Journal, 2018, 18 (6): 2453- 2461.
4	LIU M, CHEN S C, WU J, et al SAR target configuration recognition via two-stage sparse structure representation. IEEE Trans. on Geoscience and Remote Sensing, 2018, 56 (4): 2220- 22325. doi: 10.1109/TGRS.2017.2776600
5	MILLER E K, LAGER D L Inversion of one-dimensional scattering data using Prony’s method. Radio Science, 1982, 17 (1): 211- 217. doi: 10.1029/RS017i001p00211
6	POTTER L C, CHIANG D M, CARRIERE R, et al A GTD-based parametric model for radar scattering. IEEE Trans. on Antennas and Propagation, 1995, 43 (10): 1058- 1067.
7	GERRY M J, POTTER L C, GUPTA I J, et al A parametric model for synthetic aperture radar measurements. IEEE Trans. on Antennas and Propagation, 1999, 47 (7): 1179- 1188.
8	HE Y, HE S Y, ZHANG Y H, et al A forward approach to establish parametric scattering center models for known complex radar targets applied to SAR ATR. IEEE Trans. on Antennas and Propagation, 2014, 62, 6192- 6205. doi: 10.1109/TAP.2014.2360700
9	XIAO G L, GUO K L, WU B Y, et al Accurate scattering centers modeling for complex conducting targets based on induced currents. Science China (Information Sciences), 2021, 64 (2): 268- 270.
10	ERER I, GULTEKIN O, GUNEL T. Data extrapolation based CLEAN algorithm for one dimensional scattering center extraction. Proc. of the IEEE 14th Signal Processing and Communications Applications, 2006. DOI:10.1109/SIU.2006.1659774.
11	CHEN X, TIAN Y G, DONG C Z, et al. An improved state space approach based method for extracting the target scattering center. Proc. of the International Applied Computational Electromagnetics Society Symposium, 2017. DOI: 10.1007/s11432-010-4137-Z.
12	WEI S M, WANG J, SUN J P, et al Estimation of UWB radar scattering center with GTD-based 2D state-space method. Proc. of the IEEE 10th International Conference on Signal Processing, 2010, 2270- 2273.
13	LI Q F, GUO K Y, ZHAI Y Performance comparsion among different objective functions in paramter estimation of scattering center. Proc. of the International Applied Computational Electromagnetics Society Symposium, 2017, 8051928.
14	ZHANG S B, JI T T, WANG W Y, et al. Application of PSO-GA & CGA in sea-clutter Doppler spectrum modeling. Proc. of the International Joint Conference on Neural Networks, 2020, 1-8. DOI: 10.1109/IJCNN48605.2020.9207683.
15	DONELLI M, FRANCESCHINI G, MARTINI A, et al An integrated multiscaling strategy based on a particle swarm algorithm for inverse scattering problems. IEEE Trans. on Geoscience and Remote Sensing, 2006, 44 (2): 298- 312.
16	YANG M L, WU B Y, GAO H W, et al A ternary parallelization approach of MLFMA for solving electromagnetic scattering problems with over 10 billion unknowns. IEEE Trans. on Antennas and Propagation, 2019, 67 (11): 6965- 6978. doi: 10.1109/TAP.2019.2927660
17	HE S S. The analysis of nonideal scattering phenomena in radar imaging. Changsha: National University of Defense Technology, 2005.
18	GUO K Y, QU Q Y, SHENG X Q Geometry reconstruction based on attributes of scattering centers by using time-frequency representations. IEEE Trans. on Antennas and Propagation, 2016, 64 (2): 708- 720.
19	LIU H Y, TIAN G, SHI Z J The comparison of time-frequency analysis methods and their applications. Computerized Tomography Theory and Applications, 2015, 24 (2): 199- 208.
20	TAEBI A, MANSY H A. Analysis of seismocardiographic signals using polynomial chirplet transform and smoothed pseudo Wigner-Ville distribution. Proc. of the IEEE Signal Processing in Medicine and Biology Symposium, 2017. DOI: 10.1109/SPMB.2017.8257022.
21	CHEN V C, QIAN S CFAR detection and extraction of unknown signal in noise with time-frequency Gabor transform. Proc. of SPIE-The International Society for Optical Engineering, 1996, 285- 294.
22	PEI S C, HUANG S G Adaptive STFT with chirp-modulated Gaussian window. Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2018, 4354- 4358.
23	VETTERLI M, HERLEY C Wavelets and filter banks: theory and design. IEEE Trans. on Signal Processing, 1992, 40 (9): 2207- 2232. doi: 10.1109/78.157221
24	XIN N, WANG G H, ZHANG J. A synthetical pose estimation of SAR imagery using Hough transform and 2-D continuous wavelet transform. Proc. of the CIE International Conference on Radar, 2006. DOI: 10.1109/ICR.2006.343503.
25	GILLES J Empirical wavelet transform. IEEE Trans. on Signal Processing, 2013, 61 (16): 3999- 4010. doi: 10.1109/TSP.2013.2265222
26	NING J, PENG J G. Repression of the cross-term interference based on Emd and Cohen's class distribution. Proc. of the IEEE Circuits and Systems International Conference on Testing and Diagnosis, 2009. DOI: 10.1109/CAS-ICTD.2009.4960861.
27	MERLIN T, ROSHEN J, LETHAKUMARY B. Comparison of WVD based time-frequency distributions. Proc. of the International Conference on Power, Signals, Controls and Computation, 2012. DOI: 10.1109/EPSCICON.2012.6175242.
28	ZHAO X T, GUO K, SHENG X Scattering center model for edge diffraction based on EEC formula. Proc. of the Progress in Electromagnetic Research Symposium, 2016, 286- 290.
29	MILLER E K A discussion and survey of model-based parameter estimation in computational electromagnetics. Proc. of the IEEE Antennas and Propagation Society International Symposium, 1997, 3, 1980- 1983. doi: 10.1109/APS.1997.631725
30	ZHAO X T, GUO K, SHENG X Modifications on parametric models for distributed scattering centres on surfaces with arbitrary shapes. IET Radar, Sonar & Navigation, 2019, 13, 2174- 2182.
31	GORDEN W B Far-field approximations to the Kirchoff-Helmholtz representations of scattered fields. IEEE Trans. on Antennas and Propagation, 1975, 23 (4): 590- 592. doi: 10.1109/TAP.1975.1141105
32	MORAN M B H, CUNO J S, RIVEAUX J A, et al Automatic sinusoidal curves detection in borehole images using the iterated local search algorithm. Proc. of the International Conference on Systems, Signals and Image Processing, 2020, 255- 260.

Condition	Similarity of TFR images/%	Number of iterations that tend to converge	Time cost/ min
No upper and lower limits, estimate all parameters	82	72	1008
With upper and lower limits, estimate all parameters	84	30	420
With upper and lower limits, exclude partially identifiable parameters	84	24	360

Objective function used (normalized)	Similarity of TFR images/%	RMSE of RCS	Number of iterations that tend to converge
A: Similarity of TFR images	84	4.75	24
B: RMSE of RCS	79	4.42	18
A×B	83	4.58	27
A+B	82	4.42	25
2×A+B	82	4.43	20
3×A+B	82	4.44	18

Scattering center modeling for low-detectable targets

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 20

References 32

Related Articles 1

Recommended Articles

Metrics

Comments