Parameter Estimation of LFM Signals Based on Synchrosqueezing Chirplet Transform in Complicated Noise

JIN Yan; GAO Duo; JI Hongbing

doi:10.11999/JEIT161222

Volume 39 Issue 8

Aug. 2017

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(8): 1906-1912

JIN Yan, GAO Duo, JI Hongbing. Parameter Estimation of LFM Signals Based on Synchrosqueezing Chirplet Transform in Complicated Noise[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1906-1912. doi: 10.11999/JEIT161222

Citation:

JIN Yan, GAO Duo, JI Hongbing. Parameter Estimation of LFM Signals Based on Synchrosqueezing Chirplet Transform in Complicated Noise[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1906-1912. doi: 10.11999/JEIT161222

Citation:

PDF( 1132 KB)

Parameter Estimation of LFM Signals Based on Synchrosqueezing Chirplet Transform in Complicated Noise

doi: 10.11999/JEIT161222

Funds:

The National Natural Science Foundation of China (61201286), The Natural Science Foundation of Shaanxi Province (2014JM8304)

Received Date: 2016-11-10
Rev Recd Date: 2017-04-26
Publish Date: 2017-08-19

Abstract

Abstract

SynchroSqueezing Transform (SST), based on the wavelet transform, can effectively improve the energy distribution and time-frequency aggregation of a signal by compressing the wavelet coefficients in a short frequency domain. To solve the parameter estimation problem of Linear Frequency Modulation (LFM) signals, a new SynchroSqueezing Chirplet Transform (SSCT) is proposed within the framework of synchrosqueezing. Taking full use of the linear relationship between the time and the frequency of an LFM signal, the SSCT method can improve the energy density on the time-frequency plane and estimate the signal parameters accurately, which at the same time keeps the advantages of the chirplet transform, such as flexible window function selecting and no cross-term interfering. Then a Fractional Lower Order SSCT (FLOSSCT) method is proposed in order to estimate the parameters of an LFM signal in the complex noise environment. The simulation results show that the SSCT and the FLOSSCT have good performance under the background of Gaussian and impulsive noise, respectively.
- LFM signal,
- Chirplet transform,
- Synchrosqueezing transform,
- Parameter estimation,
- Alpha stable noise

FullText(HTML)

References(29)

References

金艳, 胡碧昕, 姬红兵. 稳定分布噪声下一种稳健加权滤波的统一框架[J]. 系统工程与电子技术, 2016, 38(10): 2221-2227. doi: 10.3969/j.issn.1001-506X.2016.10.01.

JIN Yan, HU Bixin, and JI Hongbing. Unified framework of robust weighted filtering in stable noise[J]. Systems Engineering and Electronics, 2016, 38(10): 2221-2227. doi: 10.3969/j.issn.1001-506X.2016.10.01.

WANG Dianwei, WANG Jing, LIU Ying, et al. An adaptive time-frequency filtering algorithm for multi-component LFM signals based on generalized S-transform[C]. 2015 21st International Conference on Automation and Computing, Glasgow, United Kingdom, 2015: 1-6.

DURAK L and ARIKAN O. Short-time Fourier transform: Two fundamental properties and an optimal implementation [J]. IEEE Transactions on Signal Processing, 2003, 51(5): 1231-1242. doi: 10.1109/TSP.2003.810293.

PEI Soochang and HUANG Shihgu. STFT with adaptive window width based on the chirp rate[J]. IEEE Transactions on Signal Processing, 2012, 60(8): 4065-4080. doi: 10.1109/ TSP.2012.2197204.

AUGER F and FLADRIN 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.

DAUBECHIES I, LU J, and WU H T. Synchrosqueezed wavelet transforms: An empirical mode decompositionlike tool[J]. Applied and Computational Harmonic Analysis, 2011, 30(2): 243-261. doi: 10.1016/j.acha.2010. 08.002.

刘景良, 任伟新, 王佐才, 等. 基于同步挤压小波变换的结构瞬时频率识别[J]. 振动与冲击, 2013, 32(18): 37-42. doi: 10.13465/j. cnki.jvs.2013.18.010.

LIU Jingliang, REN Weixin, WANG Zuocai, et al. Instantaneous frequency identification based on synchrosqueezing wavelet transformation[J]. Journal of Vibration and Shock, 2013, 32(18): 37-42. doi: 10.13465/j. cnki.jvs.2013.18.010.

HUANG Zhonglai, ZHANG Jianzhong, ZHAO Tiehu, et al. Synchrosqueezing S transform and its application in seismic spectral decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(2): 817-825. doi: 10.13465/j. cnki.jvs.2013.18.010.

MANN S and HAYKIN S. The chirplet transform: Physical considerations[J]. IEEE Transactions on Signal Processing, 1995, 43(11): 2745-2761. doi: 10.1109/78.482123.

MIKIO Aoi, KYLE Lepage, YOONSOEB Lim, et al. An approach to time-frequency analysis with ridges of the continuous Chirplet transform[J]. IEEE Transactions on Signal Processing, 2015, 63(3): 699-710. doi: 10.1109/ TSP. 2014.2365756.

邱剑锋, 谢娟, 汪继文, 等. Chirplet变换及其推广[J]. 合肥工业大学学报, 2007, 30(12): 1575-1579.

QIU Jianfeng, XIE Juan, WANG Jiwen, et al. Chirplet transform and its extension[J]. Journal of Hefei University of Technology, 2007, 30(12): 1575-1579.

王超, 任伟新, 黄天立. 基于复小波变换的结构瞬时频率识别[J]. 振动工程学报, 2009, 22(5): 492-496.

WANG Chao, REN Weixin, and HUANG Tianli. Instantaneous frequency identification of a structure based on complex wavelet transform[J]. Journal of Vibration Engineering, 2009, 22(5): 492-496.

HOU ZK, HERA A, LIU W, et al. Identification of instantaneous modal parameters of time-varying systems using wavelet approach[C]. The 4th International Workshop on Structural Health Monitoring, Stanford, 2003.

杨芳, 高静怀. Chirplet 变换中的参数选择研究[J]. 西安交通大学学报, 2007, 40(6): 719-723.

YANG Fang and GAO Jinghuai. On the choice of parameters for the Chirplet transform[J]. Journal of Xi,an Jiaotong University, 2007, 40(6): 719-723.

DAUBRCHIES I and MAES S．A Nonlinear Squeezing of the Continuous Wavelet Transform Based on Nerve Models[M]. Boca Raton: CRC Press, 1996: 527-546.

金艳, 朱敏, 姬红兵. Alpha 稳定分布噪声下基于柯西分布的相位键控信号码速率最大似然估计[J]. 电子与信息学报, 2015, 37(6): 1323-1329. doi: 10.11999/JEIT141180.

JIN Yan, ZHU Min, and JI Hongbing. Cauchy distribution based maximum-likelihood estimator for symbol rate of phase shift keying signals in alpha stable noise environment[J]. Journal of Electronics Information Technology, 2015, 37(6): 1323-1329. doi: 10.11999/JEIT141180.

邱天爽, 张旭秀, 李小兵, 等. 统计信号处理非高斯信号处理及其应用[M]. 北京: 电子工业出版社, 2004: 139-172.

QIU Tianshuang, ZHANG Xuxiu, LI Xiaobing, et al. Statistical Signal ProcessingNon-Gaussian Signal Processing and Application[M]. Beijing: Electronic Industry Press, 2004: 139-172.

郑作虎, 王首勇. 复杂海杂波背景下分数低阶匹配滤波检测方法[J] 电子学报, 2016, 44(2): 319-326. doi: 10.3969/j.issn. 0372-2112.2016.02.011.

ZHENG Zuohu and WANG Shouyong. Radar target detection method of fractional lower order matched filter in complex sea clutter background[J]. Acta Electronica Sinica,

SHAO M and NIKIAS C L. Signal processing with fractional lower order moments: Stable processes and their applications[J]. Proceedings of the IEEE, 1993, 81(7): 986-1010.

NIKIAS C L and SHAO M. Signal Processing with Alpha-stable Distribution and Application[M]. New York: John Wiley Sons, Inc, 1995: 120-128.

, 44(2): 319-326. doi: 10.3969/j.issn.0372-2112.2016.02. 011.

Relative Articles

Supplements(0)

Cited By

Proportional views