Full Text:   <3787>

CLC number: TP391.4

On-line Access: 2022-04-20

Received: 2020-09-16

Revision Accepted: 2022-05-04

Crosschecked: 2021-04-09

Cited: 0

Clicked: 6663

Citations:  Bibtex RefMan EndNote GB/T7714


Jiao BAO


Jiuwen CAO


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.4 P.515-529


Vibration-based hypervelocity impact identification and localization

Author(s):  Jiao BAO, Lifu LIU, Jiuwen CAO

Affiliation(s):  Department of Computer Engineering, Chengdu Technological University, Chengdu 611730, China; more

Corresponding email(s):   jwcao@hdu.edu.cn

Key Words:  Ensemble learning, Synchrosqueezied transform, Gray-level co-occurrence matrix, Image entropy, Distance estimation

Jiao BAO, Lifu LIU, Jiuwen CAO. Vibration-based hypervelocity impact identification and localization[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(4): 515-529.

@article{title="Vibration-based hypervelocity impact identification and localization",
author="Jiao BAO, Lifu LIU, Jiuwen CAO",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Vibration-based hypervelocity impact identification and localization
%A Jiao BAO
%A Lifu LIU
%A Jiuwen CAO
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 4
%P 515-529
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000483

T1 - Vibration-based hypervelocity impact identification and localization
A1 - Jiao BAO
A1 - Lifu LIU
A1 - Jiuwen CAO
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 4
SP - 515
EP - 529
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000483

Hypervelocity impact (HVI) vibration source identification and localization have found wide applications in many fields, such as manned spacecraft protection and machine tool collision damage detection and localization. In this paper, we study the synchrosqueezed transform (SST) algorithm and the texture color distribution (TCD) based HVI source identification and localization using impact images. The extracted SST and TCD image features are fused for HVI image representation. To achieve more accurate detection and localization, the optimal selective stitching features OSSST+TCD are obtained by correlating and evaluating the similarity between the sample label and each dimension of the features. Popular conventional classification and regression models are merged by voting and stacking to achieve the final detection and localization. To demonstrate the effectiveness of the proposed algorithm, the HVI data recorded from three kinds of high-speed bullet striking on an aluminum alloy plate is used for experimentation. The experimental results show that the proposed HVI identification and localization algorithm is more accurate than other algorithms. Finally, based on sensor distribution, an accurate four-circle centroid localization algorithm is developed for HVI source coordinate localization.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Almeida LB, 1994. The fractional Fourier transform and time-frequency representations. IEEE Trans Signal Process, 42(11):3084-3091.

[2]Anderson JLB, Schultz PH, 2006. Flow-field center migration during vertical and oblique impacts. Int J Impact Eng, 33(1-12):35-44.

[3]Auger F, Flandrin P, 1995. Improving the readability of time-frequency and time-scale representations by the reassignment method. IEEE Trans Signal Process, 43(5):1068-1089.

[4]Bai X, Tao R, Liu LJ, et al., 2012. Autofocusing of SAR images using STFRFT-based preprocessing. Electron Lett, 48(25):1622-1624.

[5]Cao JW, Wang TL, Shang LM, et al., 2018. An intelligent propagation distance estimation algorithm based on fundamental frequency energy distribution for periodic vibration localization. J Franklin Inst, 355(4):1539-1558.

[6]Cao JW, Zhang K, Yong HW, et al., 2019. Extreme learning machine with affine transformation inputs in an activation function. IEEE Trans Neur Netw Learn Syst, 30(7):2093-2107.

[7]Cao JW, Dai HZ, Lei BY, et al., 2020. Maximum correntropy criterion-based hierarchical one-class classification. IEEE Trans Neur Netw Learn Syst, 7:1-7.

[8]Chan HL, Huang HH, Lin JL, 2001. Time-frequency analysis of heart rate variability during transient segments. Ann Biomed Eng, 29(11):983-996.

[9]Cohen L, 2013. leID1. Time-frequency analysis: theory and applications. J Acoust Soc Am, 134(5):4002.

[10]Daubechies I, 1990. The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inform Theory, 36(5):961-1005.

[11]Daubechies I, Maes S, 1996. A nonlinear squeezing of the continuous wavelet transform based on auditory nerve models. In: Aldroubi A, Unser M (Eds.), Wavelets in Medicine and Biology. CRC-Press, Boca Raton, USA.

[12]Daubechies I, Lu JF, Wu HT, 2011. Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmon Anal, 30(2):243-261.

[13]Erickson AS, 2014. China's space development history: a comparison of the rocket and satellite sectors. Acta Astronaut, 103:142-167.

[14]Flandrin P, Amin M, McLaughlin S, et al., 2013. Time-frequency analysis and applications. IEEE Signal Process Mag, 30(6):19-150.

[15]Franco C, Guméry PY, Vuillerme N, et al., 2012. Synchrosqueezing to investigate cardio-respiratory interactions within simulated volumetric signals. Proc 20th European Signal Processing Conf, p.939-943.

[16]Ghosh SK, Tripathy RK, Ponnalagu RN, et al., 2019. Automated detection of heart valve disorders from the PCG signal using time-frequency magnitude and phase features. IEEE Sens Lett, 3(12):7002604.

[17]Huang XG, Yin C, Huang J, et al., 2016. Hypervelocity impact of TiB2-based composites as front bumpers for space shield applications. Mater Des, 97:473-482.

[18]Huang XG, Yin C, Ru HQ, et al., 2020. Hypervelocity impact damage behavior of B4C/Al composite for MMOD shielding application. Mater Des, 186:108323.

[19]Liou JC, Johnson NL, 2006. Risks in space from orbiting debris. Science, 311(5759):340-341.

[20]Liu LF, Cao JW, Huang XG, 2019. High speed collision localization based on surface vibration processing. Proc 12th Int Symp on Computational Intelligence and Design, p.35-38.

[21]Mallat SG, 1989. Theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Patt Anal Mach Intell, 11(7):674-693.

[22]Mamli S, Kalbkhani H, 2019. Gray-level co-occurrence matrix of Fourier synchro-squeezed transform for epileptic seizure detection. Biocybern Biomed Eng, 39(1):87-99.

[23]Materka A, Strzelecki M, 1998. Texture Analysis Methods—a Review. COST B11 Report. University of Lodz, Brussels, Belgium.

[24]Millan RM, von Steiger R, Ariel M, 2019. Small satellites for space science: a cospar scientific roadmap. Adv Space Res, 64(8):1466-1517.

[25]Mirzapour F, Ghassemian H, 2013. Using GLCM and Gabor filters for classification of PAN images. 21st Iranian Conf on Electrical Engineering, p.1-6.

[26]Monti A, Medigue C, Mangin L, 2002. Instantaneous parameter estimation in cardiovascular time series by harmonic and time-frequency analysis. IEEE Trans Biomed Eng, 49(12):1547-1556.

[27]Önsay T, Haddow AG, 1993. Comparison of STFT, Gabor, and wavelet transforms in transient vibration analysis of mechanical systems. J Acoust Soc Am, 93(4):2290.

[28]Pierazzo E, Melosh HJ, 2000. Hydrocode modeling of oblique impacts: the fate of the projectile. Meteorit Planet Sci, 35(1):117-130.

[29]Qian SE, Chen DP, 1999. Joint time-frequency analysis. IEEE Signal Process Mag, 16(2):52-67.

[30]Shang LM, Cao JW, Wang JZ, et al., 2016. Fundamental frequency energy distribution of periodic vibrations and their relation to distance. Proc IEEE 13th Int Conf on Signal Processing, p.96-101.

[31]Stankovic L, Stankovic S, Dakovic M, 2014. From the STFT to the Wigner distribution. IEEE Signal Process Mag, 31(3):163-174.

[32]Tao R, Li YL, Wang Y, 2010. Short-time fractional Fourier transform and its applications. IEEE Trans Signal Process, 58(5):2568-2580.

[33]Thakur G, Brevdo E, Fučkar NS, et al., 2013. The synchrosqueezing algorithm for time-varying spectral analysis: robustness properties and new paleoclimate applications. Signal Process, 93(5):1079-1094.

[34]Torrence C, Compo G, 1998. A practical guide to wavelet analysis. Bull Amer Meteorol Soc, 79(79):61-78.

[35]Wang TL, Cao JW, Lai XP, et al., 2020. Hierarchical one-class classifier with within-class scatter-based autoencoders. IEEE Trans Neur Netw Learn Syst, 32(8):3770-3776.

[36]Wilson EK, 2019. Space tourism moves closer to lift off. Engineering, 5(5):819-821.

[37]Witze A, 2018. The quest to conquer Earth's space junk problem. Nature, 561(7721):24-26.

[38]Yang MQ, Kpalma K, Ronsin J, 2008. A survey of shape feature extraction techniques. Patt Recogn, 15(7):43-90.

[39]Yin C, Xue T, Huang XG, et al., 2019. Research on damages evaluation method with multi-objective feature extraction optimization scheme for M/OD impact risk assessment. IEEE Access, 7:98530-98545.

[40]Zhou ZH, 2016. Machine Learning. Tsinghua University Press, Beijing, China (in Chinese).

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE