一种低副瓣无混叠的线性调频信号时频分析方法

刘会杰; 高新海; 郭汝江

doi:10.11999/JEIT181190

一种低副瓣无混叠的线性调频信号时频分析方法

doi: 10.11999/JEIT181190

刘会杰¹,
高新海^{1, 2, 3, ,},
郭汝江¹

1.
中国科学院微小卫星创新研究院上海 201203
2.
中国科学院上海微系统与信息技术研究所上海 200050
3.
中国科学院大学北京 100049

基金项目: 国家自然科学基金(91738201)，上海市青年科技英才扬帆计划(17YF1418200)

详细信息

作者简介:
刘会杰：男，1972年生，博士，研究员，博士生导师，研究方向为卫星移动通信系统理论与通信系统设计

高新海：男，1995年生，硕士生，研究方向为复杂电磁环境下的时频分析

郭汝江：男，1977年生，博士，研究员，研究方向为新体制雷达、保密通信、无源定位

通讯作者:
高新海　gxh777@mail.ustc.edu.cn

中图分类号: TN911
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出版历程
- 收稿日期: 2018-12-28
- 修回日期: 2019-05-27
- 网络出版日期: 2019-08-23
- 刊出日期: 2019-11-01

A Time-frequency Analysis Method for Linear Frequency Modulation Signal with Low Sidelobe and Nonaliasing Property

Huijie LIU¹,
Xinhai GAO^{1, 2, 3
, ,},
Rujiang GUO¹

1.
Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201203, China
2.
Shanghai Institute of Microsystem and Information Technology, Shanghai 200050, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The National Natural Science Foundation of China (91738201), Shanghai Sailing Program (17YF1418200)

摘要

摘要: 作为通信与勘探中广泛使用的一类信号，线性调频信号的参数分析经常采用基于Wigner-Ville分布(WVD)的时频分析方法。该方法具有高时频分辨率，但在交叉项、高副瓣以及频谱混叠问题上存在缺陷。该文提出一种名为空间变迹重排Wigner-Ville分布(SVA-rWVD)的时频分析方法，结合空间变迹技术(SVA)的副瓣抑制能力及短时傅里叶变换(STFT)的无混叠无交叉项特性，得到一个新的时频分布。基于单分量和多分量线性调频信号的仿真实验结果表明，该方法得到的时频分布可以降低副瓣水平至–40 dB以下同时消除交叉项及频谱混叠现象。
- 时频分析 /
- 线性调频信号 /
- Wigner-Ville分布 /
- 空间变迹 /
- 短时傅里叶变换
Abstract: Chirp signals are widely used in communication and exploration. The parameter analysis of the chirp signals often uses a Wigner-Ville Distribution (WVD) based time-frequency analysis method, which achieves high time-frequency resolution. However, this method has defects in cross terms, high sidelobes, and spectral aliasing problems. To solve these problems, a time-frequency analysis method called Spatially Variant Apodiztion-rearrange Wigner Ville Distribution (SVA-rWVD) is proposed, which achieves low sidelobes by exploiting the Spatially Variant Apodization (SVA) techniques, and avoids the cross terms and the spectral aliasing problems by applying the Short Time Fourier Transform (STFT). Furthermore, a new time-frequency distribution is obtained from the proposed method. Extensive simulations show that the time-frequency distribution obtained by the proposed method not only reduces the sidelobe level to –40 dB but also eliminates cross terms and spectral aliasing for both single-component and multi-component chirp signals.
- Time-frequency analysis /
- Linear Frequency Modulation (LFM) /
- Wigner-Ville Distribution (WVD) /
- Spatially Variant Apodization (SVA) /
- Short Time Fourier Transform (STFT)

HTML全文

图 1 单分量LFM信号的时频分析结果对比

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图 2 时间切片比较结果(取对数进行分析)

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图 3 SVA-rWVD方法消除混叠现象的原理

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图 4 多分量LFM信号的时频分析结果对比

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图 5 时间切片比较结果(取对数进行分析)

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图 6 多分量LFM信号的时频分析结果对比

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图 7 时间切片比较结果(取对数进行分析)

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图 8 不同信噪比下瞬时频率估计的均方根误差

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表 1 仿真参数设置

参数名称	单分量LFM(图1)	多分量LFM(图4)	多分量LFM(图6)
脉冲宽度(μs)	1024	1024	1204
带宽(MHz)	1	1	1
采样率(MHz)	1	1	1
时间切片(μs)	400	500	300
频率范围(kHz)	–500～500	3个分量均为–500～500，且时间间隔相同	–300～–100 0～300 –500～500

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参考文献(19)

YI Wei, CHEN Zhenhua, HOSEINNEZHAD R, et al. Joint estimation of location and signal parameters for an LFM emitter[J]. Signal Processing, 2017, 134: 100–112. doi: 10.1016/j.sigpro.2016.11.014

李秀坤, 吴玉双. 多分量线性调频信号的Wigner-Ville分布交叉项去除[J]. 电子学报, 2017, 45(2): 315–320. doi: 10.3969/j.issn.0372-2112.2017.02.008

LI Xiukun and WU Yushuang. Cross-term removal of Wigner-Ville distribution for multi-component LFM signals[J]. Acta Electronica Sinica, 2017, 45(2): 315–320. doi: 10.3969/j.issn.0372-2112.2017.02.008

KUMAR R, ZHAO Wei, and SINGH V. Joint time-frequency analysis of seismic signals: A critical review[J]. Structural Durability & Health Monitoring, 2018, 12(2): 65–83. doi: 10.3970/sdhm.2018.02329

邹红星, 戴琼海, 李衍达, 等. 不含交叉项干扰且具有WVD聚集性的时频分布之不存在性[J]. 中国科学: E辑, 2002, 45(3): 174–180. doi: 10.1360/02yf9015

ZOU Hongxing, DAI Qionghai, LI Yanda, et al. Nonexistence of cross-term free time-frequency distribution with concentration of Wigner-Ville distribution[J]. Science in China Series F:Information Sciences, 2002, 45(3): 174–180. doi: 10.1360/02yf9015

BOASHASH B, ALI KHAN N, and BEN-JABEUR T. Time-frequency features for pattern recognition using high-resolution TFDs: A tutorial review[J]. Digital Signal Processing, 2015, 40: 1–30. doi: 10.1016/j.dsp.2014.12.015

赵培洪, 平殿发, 邓兵, 等. 魏格纳-维尔分布交叉项抑制方法综述[J]. 探测与控制学报, 2010, 32(1): 23–29. doi: 10.3969/j.issn.1008-1194.2010.01.006

ZHAO Peihong, PING Dianfa, DENG Bing, et al. Review of cross-terms suppression methods in Wigner-Ville distribution[J]. Journal of Detection &Control, 2010, 32(1): 23–29. doi: 10.3969/j.issn.1008-1194.2010.01.006

ALI KHAN N and SANDSTEN M. Time-frequency image enhancement based on interference suppression in Wigner-Ville distribution[J]. Signal Processing, 2016, 127: 80–85. doi: 10.1016/j.sigpro.2016.02.027

REN Huorong, REN An, and LI Zhiwu. A new strategy for the suppression of cross-terms in pseudo Wigner-Ville distribution[J]. Signal, Image and Video Processing, 2016, 10(1): 139–144. doi: 10.1007/s11760-014-0713-9

BOASHASH B and OUELHA S. An improved design of high-resolution quadratic time-frequency distributions for the analysis of nonstationary multicomponent signals using directional compact kernels[J]. IEEE Transactions on Signal Processing, 2017, 65(10): 2701–2713. doi: 10.1109/TSP.2017.2669899

PACHORI R B and NISHAD A. Cross-terms reduction in the Wigner-Ville distribution using tunable-Q wavelet transform[J]. Signal Processing, 2016, 120: 288–304. doi: 10.1016/j.sigpro.2015.07.026

王见, 李金同, 卢华玲, 等. 采用STFT-Wigner变换抑制Wigner-Ville分布交叉项[J]. 重庆大学学报, 2013, 36(8): 15–18. doi: 10.11835/j.jssn.1000-582X.2013.08.003

WANG Jian, LI Jintong, LU Hualing, et al. Using STFT-Wigner transform to suppress the cross terms in Wigner-Ville distribution[J]. Journal of Chongqing University, 2013, 36(8): 15–18. doi: 10.11835/j.jssn.1000-582X.2013.08.003

STANKWITZ H C, DALLAIRE R J, and FIENUP J R. Nonlinear apodization for sidelobe control in SAR imagery[J]. IEEE Transactions on Aerospace and Electronic Systems, 1995, 31(1): 267–279. doi: 10.1109/7.366309

GUO Liang, YIN Hongfei, ZHOU Yu, et al. A novel sidelobe-suppression algorithm for airborne synthetic aperture imaging ladar[J]. Optics & Laser Technology, 2019, 111: 714–719. doi: 10.1016/j.optlastec.2018.09.005

徐光耀, 刘永泽, 许小剑. 基于变迹滤波的MIMO-SAR图像分辨率增强技术[J]. 北京航空航天大学学报, 2017, 43(7): 1313–1320. doi: 10.13700/j.bh.1001-5965.2016.0514

XU Guangyao, LIU Yongze, and XU Xiaojian. Enhanced resolution in MIMO-SAR imaging using apodization[J]. Journal of Beijing University of Aeronautics and Astronsutics, 2017, 43(7): 1313–1320. doi: 10.13700/j.bh.1001-5965.2016.0514

NI Chong, WANG Yanfei, XU Xianghui, et al. A super-resolution algorithm for synthetic aperture radar based on modified spatially variant apodization[J]. Science China Physics, Mechanics and Astronomy, 2011, 54(2): 355–364. doi: 10.1007/s11433-010-4186-8

SARKAR B, PANIGRAHI R K, and MISHRA A K. Sidelobe suppression in Wigner distribution using non-linear apodization[C]. Proceedings of 2009 Annual IEEE India Conference, Gujarat, India, 2009: 1–4. doi: 10.1109/INDCON.2009.5409393.

SUN Kewen, ZHANG Min, and YANG Dongkai. A new interference detection method based on joint hybrid time-frequency distribution for GNSS receivers[J]. IEEE Transactions on Vehicular Technology, 2016, 65(11): 9057–9071. doi: 10.1109/TVT.2016.2515718

刘颖, 陈殿仁, 陈磊, 等. 基于周期Choi-Williams Hough变换的线性调频连续波信号参数估计算法[J]. 电子与信息学报, 2015, 37(5): 1135–1140. doi: 10.11999/JEIT140876

LIU Ying, CHEN Dianren, CHEN Lei, et al. Parameters estimation algorithm of linear frequency modulated continuous wave signals based on period Choi-Williams Hough transform[J]. Journal of Electronics &Information Technology, 2015, 37(5): 1135–1140. doi: 10.11999/JEIT140876

张天骐, 全盛荣, 强幸子, 等. 基于多尺度Chirplet稀疏分解和Wigner-Ville变换的时频分析方法[J]. 电子与信息学报, 2017, 39(6): 1333–1339. doi: 10.11999/JEIT160750

ZHANG Tianqi, QUAN Shengrong, QIANG Xingzi, et al. Time-frequency analysis method based on multi-scale Chirplet sparse decomposition and Wigner-Ville transform[J]. Journal of Electronics &Information Technology, 2017, 39(6): 1333–1339. doi: 10.11999/JEIT160750

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