高质量LMSCT时频分析算法及其在雷达信号目标检测中的应用

doi:10.16183/j.cnki.jsjtu.2020.432

上海交通大学学报 ›› 2022, Vol. 56 ›› Issue (2): 231-241.doi: 10.16183/j.cnki.jsjtu.2020.432

高质量LMSCT时频分析算法及其在雷达信号目标检测中的应用

郝国成¹^,²^,³, 张必超¹, 锅娟¹, 张雅冰¹, 石光耀¹, 王盼盼¹, 张薇¹()

1. 中国地质大学(武汉) 机械与电子信息学院,武汉 430074
2. 杜克大学数学系,美国Durham 27708
3. 中国地质大学(武汉) 复杂系统先进控制与智能自动化湖北重点实验室, 武汉 430074

收稿日期:2020-12-19 出版日期:2022-02-28 发布日期:2022-03-03
通讯作者: 张薇 E-mail:2569691867@qq.com
作者简介:郝国成(1975-),男,山东省聊城市人,副教授,博士生导师,主要从事非平稳信号时频分析算法研究.
基金资助:
武汉市科技局攻关项目(2016060101010073);高等学校学科创新引智计划(B17040);大地测量与地球动力学国家重点实验室开放基金(SKLGED2018-5-4-E);复杂系统先进控制与智能自动化湖北省重点实验室基金(ACIA2017002);智能地学信息处理湖北省重点实验室开放课题资助项目(KLIGIP2017A01)

A High Quality Algorithm of Time-Frequency Analysis and Its Application in Radar Signal Target Detection via LMSCT

HAO Guocheng¹^,²^,³, ZHANG Bichao¹, GUO Juan¹, ZHANG Yabing¹, SHI Guangyao¹, WANG Panpan¹, ZHANG Wei¹()

1.School of Mechanical Engineering and Electronic Information, China University of Geosciences (Wuhan), Wuhan 430074, China
2.Department of Mathematics, Duke University, Durham 27708, USA
3.Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, China University of Geosciences (Wuhan), Wuhan 430074, China

Received:2020-12-19 Online:2022-02-28 Published:2022-03-03
Contact: ZHANG Wei E-mail:2569691867@qq.com

摘要/Abstract

摘要：

针对线性调频小波变换(CT)引入的调频率参数不能完全匹配信号的瞬时频率及算法抗噪性能不佳等问题,提出高质量的局部最大值同步压缩线调频小波变换 (LMSCT) 算法,改善CT时频分布图能量扩散幅度出现的偏差.所提算法的核心思想是通过局部最大值同步压缩操作重新分配CT的频率点.实验结果表明,LMSCT算法具有较高的时频聚集度,并且能够较好地抑制噪声的干扰,在低信噪比的情况下仍然保持良好的时频聚集度.在IPIX处理雷达信号分析中,LMSCT 算法能够较为清晰地描绘目标信号的时间-频率联合分布特性,并且确定目标出现的距离单元,为海杂波背景下的IPIX雷达信号小目标检测提供判断依据.

关键词: 时频分析, 线调频小波变换, 局部最大值同步压缩变换, IPIX雷达信号

Abstract:

Aimed at the fact that the chirplet rate parameter of the chirplet transform (CT) cannot match the instantaneous frequency of the signal completely, and that the anti-noise performance of the algorithm is poor, this paper proposes a high-quality local maximum synchrosqueezing chirplet transform (LMSCT) algorithm to improve the deviation of energy diffusion amplitude in CT time-frequency (TF)distribution. The main idea of this algorithm is to reallocate CT frequency points by local maximum synchrosqueezing operation. The experiment results show that the LMSCT algorithm has a higher TF concentration and a strong ability to suppress the interference of noise. The method can maintain a better resolution of TF representation at a low signal-to-noise ratio. In the application analysis of IPIX processing radar signals, the LMSCT algorithm can clearly describe the TF joint distribution characteristic of target signal and determine the distance unit of target, which provides the judgement basis for small target detection of IPIX radar signal in the background of sea clutter.

Key words: time-frequency (TF) analysis, chirplet transform (CT), local maximum synchrosqueezing transform, IPIX processing radar signal

中图分类号:

TN911.72

郝国成, 张必超, 锅娟, 张雅冰, 石光耀, 王盼盼, 张薇. 高质量LMSCT时频分析算法及其在雷达信号目标检测中的应用[J]. 上海交通大学学报, 2022, 56(2): 231-241.

HAO Guocheng, ZHANG Bichao, GUO Juan, ZHANG Yabing, SHI Guangyao, WANG Panpan, ZHANG Wei. A High Quality Algorithm of Time-Frequency Analysis and Its Application in Radar Signal Target Detection via LMSCT[J]. Journal of Shanghai Jiao Tong University, 2022, 56(2): 231-241.

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参考文献 26

[1]	SEJDI$\acute{C}$ E, DJUROVI$\acute{C}$ I, JIANG J. Time-frequency feature representation using energy concentration: An overview of recent advances[J]. Digital Signal Processing, 2009, 19(1):153-183. doi: 10.1016/j.dsp.2007.12.004 URL
[2]	李雄飞, 宋璐, 张小利. 基于协同经验小波变换的遥感图像融合[J]. 吉林大学学报(工学版), 2019, 49(4):1307-1319.
	LI Xiongfei, SONG Lu, ZHANG Xiaoli. Remote sensing image fusion based on cooperative empirical wavelet transform[J]. Journal of Jilin University (Engineering and Technology Edition), 2019, 49(4):1307-1319.
[3]	HAO G C, TAN F, HU X Y, et al. A matching pursuit-based method for cross-term suppression in WVD and its application to the ENPEMF[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(8):1304-1308. doi: 10.1109/LGRS.8859 URL
[4]	HAO G C, BAI Y X, LIU H, et al. The earth’s natural pulse electromagnetic fields for earthquake time-frequency characteristics: Insights from the EEMD-WVD method[J]. Island Arc, 2018, 27(4):e12256. doi: 10.1111/iar.2018.27.issue-4 URL
[5]	YU G, YU M J, XU C Y. Synchroextracting transform[J]. IEEE Transactions on Industrial Electronics, 2017, 64(10):8042-8054. doi: 10.1109/TIE.2017.2696503 URL
[6]	郝国成, 谈帆, 程卓, 等. 强鲁棒性和高锐化聚集度的BGabor-NSPWVD时频分析算法[J]. 自动化学报, 2019, 45(3):566-576.
	HAO Guocheng, TAN Fan, CHENG Zhuo, et al. Time-frequency analysis of BGabor-NSPWVD algorithm with strong robustness and high sharpening concentration[J]. Acta Automatica Sinica, 2019, 45(3):566-576.
[7]	WANG S B, CHEN X F, WANG Y, et al. Nonlinear squeezing time-frequency transform for weak signal detection[J]. Signal Processing, 2015, 113:195-210. doi: 10.1016/j.sigpro.2015.01.022 URL
[8]	GABOR D. Theory of communication. Part 1: The analysis of information[J]. Journal of the Institution of Electrical Engineers-Part III: Radio and Communication Engineering, 1946, 93(26):429-441. doi: 10.1049/ji-3-2.1946.0074 URL
[9]	DAUBECHIES I. Ten lectures on wavelets[M]. Philadelphia, PA,USA: Society for Industrial and Applied Mathematics, 1992.
[10]	BOASHASH B, BLACK P. An efficient real-time implementation of the Wigner-Ville distribution[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1987, 35(11):1611-1618. doi: 10.1109/TASSP.1987.1165070 URL
[11]	MANN S, HAYKIN S. The chirplet transform: Physical considerations[J]. IEEE Transactions on Signal Processing, 1995, 43(11):2745-2761. doi: 10.1109/78.482123 URL
[12]	YU G, ZHOU Y Q. General linear chirplet transform[J]. Mechanical Systems and Signal Processing, 2016, 70/71:958-973. doi: 10.1016/j.ymssp.2015.09.004 URL
[13]	AUGER F, FLANDRIN P. Improving the readability of time-frequency and time-scale representations by the reassignment method[J]. IEEE Transactions on Signal Processing, 1995, 43(5):1068-1089. doi: 10.1109/78.382394 URL
[14]	DAUBECHIES I, LU J F, WU H T. Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool[J]. Applied and Computational Harmonic Analysis, 2011, 30(2):243-261. doi: 10.1016/j.acha.2010.08.002 URL
[15]	THAKUR G, WU H T. Synchrosqueezing-based recovery of instantaneous frequency from nonuniform samples[J]. SIAM Journal on Mathematical Analysis, 2011, 43(5):2078-2095. doi: 10.1137/100798818 URL
[16]	OBERLIN T, MEIGNEN S, PERRIER V. Second-order synchrosqueezing transform or invertible reassignment? Towards ideal time-frequency representations[J]. IEEE Transactions on Signal Processing, 2015, 63(5):1335-1344. doi: 10.1109/TSP.2015.2391077 URL
[17]	BEHERA R, MEIGNEN S, OBERLIN T. Theoretical analysis of the second-order synchrosqueezing transform[J]. Applied and Computational Harmonic Analysis, 2018, 45(2):379-404. doi: 10.1016/j.acha.2016.11.001 URL
[18]	LI C, LIANG M. Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform[J]. Mechanical Systems and Signal Processing, 2012, 26:205-217. doi: 10.1016/j.ymssp.2011.07.001 URL
[19]	LI C, LIANG M. A generalized synchrosqueezing transform for enhancing signal time-frequency representation[J]. Signal Processing, 2012, 92(9):2264-2274. doi: 10.1016/j.sigpro.2012.02.019 URL
[20]	PHAM D H, MEIGNEN S. High-order synchrosqueezing transform for multicomponent signals analysis—With an application to gravitational-wave signal[J]. IEEE Transactions on Signal Processing, 2017, 65(12):3168-3178. doi: 10.1109/TSP.2017.2686355 URL
[21]	YU G, WANG Z H, ZHAO P, et al. Local maximum synchrosqueezing transform: An energy-concentrated time-frequency analysis tool[J]. Mechanical Systems and Signal Processing, 2019, 117:537-552. doi: 10.1016/j.ymssp.2018.08.006 URL
[22]	ZHU X X, ZHANG Z S, LI Z, et al. Multiple squeezes from adaptive chirplet transform[J]. Signal Processing, 2019, 163:26-40. doi: 10.1016/j.sigpro.2019.05.008 URL
[23]	PENG Z K, MENG G, CHU F L, et al. Polynomial chirplet transform with application to instantaneous frequency estimation[J]. IEEE Transactions on Instrumentation and Measurement, 2011, 60(9):3222-3229. doi: 10.1109/TIM.2011.2124770 URL
[24]	BARANIUK R G, FLANDRIN P, JANSSEN A J E M, et al. Measuring time-frequency information content using the Renyi entropies[J]. IEEE Transactions on Information Theory, 2001, 47(4):1391-1409. doi: 10.1109/18.923723 URL
[25]	WILLIAMS W J, BROWN M L, HERO A O. Uncertainty, information, and time-frequency distributions[C]//Advanced Signal Processing Algorithms, Architectures, and Implementations II. San Diego, CA, USA: SPIE, 1991: 144-156.
[26]	HAYKIN S, BHATTACHARYA T K. Modular learning strategy for signal detection in a nonstationary environment[J]. IEEE Transactions on Signal Processing, 1997, 45(6):1619-1637. doi: 10.1109/78.600003 URL

时频分析算法	Rényi熵
STFT	17.4814
CT	17.2184
LMSCT	11.6583
SST	12.2263

时频分析算法	Rényi熵
STFT	14.8945
CT	14.5068
LMSCT	9.7202
SST	10.3494

IPIX雷达参数	参数值
风向/(°)	300
风速/(km·h^-1)	19
浪高/m	0.7
距离分辨率/m	30
采样间隔/m	15
目标单元	8
受影响单元	7、9、10

高质量LMSCT时频分析算法及其在雷达信号目标检测中的应用

A High Quality Algorithm of Time-Frequency Analysis and Its Application in Radar Signal Target Detection via LMSCT

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