基于局域线性法和卡尔曼滤波的强混沌背景下正弦信号恢复的混合算法

doi:10.3969/j.issn.0372-2112.2017.04.011

电子学报 ›› 2017, Vol. 45 ›› Issue (4): 837-843.DOI: 10.3969/j.issn.0372-2112.2017.04.011

基于局域线性法和卡尔曼滤波的强混沌背景下正弦信号恢复的混合算法

苏理云, 孙唤唤, 李晨龙

重庆理工大学数学与统计学院, 重庆 400054

收稿日期:2016-01-12 修回日期:2016-07-27 出版日期:2017-04-25
- 作者简介:
- 苏理云 男,1977年12月出生,四川广安人.2007年在四川大学数学学院获博士学位,现为重庆理工大学数学与统计学院副教授,主要从事统计信号检测与估计、弱信号检测、大数据分析研究.E-mail:cloudhopping@163.com;孙唤唤 女,1990年6月出生,江苏徐州人.现为重庆理工大学数学与统计学院统计学专业研究生,主要研究方向为统计信号检测与估计.E-mail:sunhuanjiayou@126.com
- 基金资助:
- 国家自然科学基金 (No.11471060）; 重庆市科委基础与前沿研究项目 (No.cstc2014jcyjA40003）

LL-P-KF Hybrid Algorithm for Detecting and Recovering Sinusoidal Signal in Strong Chaotic Noise

SU Li-yun, SUN Huan-huan, LI Chen-long

School of Mathematics and Statistics, Chongqing University of Technology, Chongqing 400054, China

Received:2016-01-12 Revised:2016-07-27 Online:2017-04-25 Published:2017-04-25

摘要/Abstract

摘要：

本文提出一种局域线性-周期图检测-卡尔曼滤波混合算法LL-P-KF，用来恢复强混沌背景下正弦信号.首先，建立混沌信号的一步预测模型；然后，利用周期图检测潜在周期；最后，应用卡尔曼滤波从误差中恢复正弦信号.仿真实验表明该混合算法具有较好的恢复效果.

关键词: 混沌信号, 局域线性法, 弱正弦信号, 周期图法, 卡尔曼滤波

Abstract:

The paper proposes a local linear-periodogram-Kalman filtering (LL-P-KF) hybrid algorithm.Firstly,a local linear method is established;secondly,the periods of sinusoidal signal are detected from modeling error by using periodogram;finally,the sinusoidal signal is recovered from the error by using Kalman filtering.Simulation results show that the LL-P-KF hybrid algorithm has better recovery results.

Key words: chaotic signal, local linear prediction, weak sinusoidal signal, periodogram, Kalman filtering

中图分类号:

苏理云, 孙唤唤, 李晨龙. 基于局域线性法和卡尔曼滤波的强混沌背景下正弦信号恢复的混合算法[J]. 电子学报, 2017, 45(4): 837-843.

SU Li-yun, SUN Huan-huan, LI Chen-long. LL-P-KF Hybrid Algorithm for Detecting and Recovering Sinusoidal Signal in Strong Chaotic Noise[J]. Acta Electronica Sinica, 2017, 45(4): 837-843.

参考文献

[1] Leung H,Huang X P.Parameter estimation in chaotic noise[J].IEEE Transactions on Signal Processing,1996,44(10):2456-2463.
[2] K M Short.Steps toward unmasking secure communications[J].International Journal of Bifurcation and Chaos,1994,4(4):959-977.
[3] K M Short.Unmasking a modulated chaotic communications scheme[J].International Journal of Bifurcation and Chaos,1996,6(2):367-375.
[4] K M Short.Signal extraction from chaotic communications[J].International Journal of Bifurcation and Chaos,1997,7(7):1579-1597.
[5] Broomhead D S,Huke J P,Potts A S.Cancelling deterministic noise by constructing nonlinear inverses to linear filters[J].Physical D,1996,89(3):439-458.
[6] Huang Xian Gao,Xu Jian Xue.Unmasking chaotic mask by a wavelet multiscale decomposition algorithm[J].International Journal of Bifurcation and Chaos,2001,11(2):561-569.
[7] Wang G,Lin J,Chen X,et al.The application of chaotic oscillators to weak signal detection[J].IEEE Transactions on Industrial Electronics,1999,46(2):440-444.
[8] Wang G,Zheng W,He S.Estimation of amplitude and phase of a weak signal by using the property of sensitive dependence on initial conditions of a nonlinear oscillator[J].Signal Process,2002,82(1):103-115.
[9] 汪芙平,郭静波,王赞基,萧达川.强混沌干扰中的谐波信号提取[J].物理学报,2001,50(6):1019-1023. Wang F P,Guo J B,Wang Z J,et al.Harmonic signal extraction from strong chaotic interference[J].Acta Physica Sinica,2001,50(6):1019-1023.(in Chinese)
[10] 李鸿光,孟光.基于经验模式分解的混沌下谐波信号的提取方法[J].物理学报,2004,53(7):2069-2073. Li Hong-Guang,Meng Guang.Harmonic signal extraction from chaotic interference based on empirical mode decomposition[J].Acta Physica Sinica,2004,53(7):2069-2073.(in Chinese)
[11] 李月,徐凯,杨宝俊,等.混沌振子系统周期解几何特征量分析与微弱周期信号的定量检测[J].物理学报,2008,57(6):3353-3358. Li Y,Xu K,Yang B J,et al.Analysis of the geometric characteristic quantity of the periodic solutions of the chaotic oscillator system and the quantitative detection of weak periodic signal[J].Acta Physica Sinica,2008,57(6):3353-3358.(in Chinese)
[12] 李月,杨宝俊,林红波,等.基于特定混沌系统微弱谐波信号频率检测的理论分析与仿真[J].物理学报,2005,54(5):1994-1999. Li Y,Yang B J,Lin H B,et al.Simulation and theoretical analysis on detection of the frequency of weak harmonic signals based on a special chaotic system[J].Acta Physica Sinica,2005,54(5):1994-1999.(in Chinese)
[13] 李月,杨宝俊,石要武.色噪声背景下微弱正弦信号的混沌检测[J].物理学报,2003,52(3):526-530. Li Y,Yang B J,Shi Y W.Chaos-based weak sinusoidal signal detection approach under colored noise background[J].Acta Physica Sinica,2003,52(3):526-530.(in Chinese)
[14] Liu Q Z,Song W Q.Detection weak period signal using chaotic oscillator[A].Computational Science and Its Applications (ICCSA 2009)[C].Berlin:Springer,2009.685-692.
[15] 陈争,曾以成,付志坚.混沌背景中信号参数估计的新方法[J].物理学报,2008,57(1):46-50. Chen Zheng,Zeng Yi Cheng,Fu Zhi Jian.A novel parameter estimation method of signal in chaotic background[J].Acta Physica Sinica,2008,57(1):46-50.(in Chinese)
[16] 孙晓东,石要武,于晓辉.混沌干扰背景下的正弦频率估计新方法[J].吉林大学学报(工学版),2009,39(5):1353-1357. SUN Xiao-dong,SHI Yao-wu,YU Xiao-hui.New frequency estimation method for sinusoidal signal submerged in strong chaotic background[J].Journal of Jilin University (Engineering and Technology Edition),2009,39(5):1353-1357.(in Chinese)
[17] 金天,张骅.基于统计方法的混沌Duffing振子弱信号检测与估计[J].中国科学:信息科学,2011,41(10):1184-1199. JIN Tian,ZHANG Hua.Statistical approach to weak signal detection and estimation using Duffing chaotic oscillators[J].Science China:Information Sciences,2011,41(10):1184-1199.(in Chinese)
[18] Haykin S,Li X B.Detection of signals in chaos[J].Proceedings of the IEEE,1995,83(1):95-122.
[19] 刘海波,吴德伟,戴传金,毛虎.基于Duffing振子的弱正弦信号检测方法研究[J].电子学报,2013,41(1):1-8. LIU Hai-bo,WU De-wei,DAI Chuan-jin,MAO Hu.A new weak sinusoidal signal detection method based on Duffing oscillators[J].Acta Electronica Sinica,2013,41(1):1-8.(in Chinese)
[20] 行鸿彦,徐伟.混沌背景中微弱信号检测的神经网络方法[J].物理学报,2007,56(7):3771-3776. Xing Hong-Yan,Xu Wei.The neural networks method for detecting weak signals under chaotic background[J].Acta Physica Sinica,2007,56(7):3771-3776.(in Chinese)
[21] 何丽桥,王国光,王丹.提取混沌中微弱信号的正交投影方法[J].吉林大学学报(工学版),2008,38(4):950-954. HE Li-qiao,WANG Guo-guang,WANG Dan.Journal of Jilin University (Engineering and Technology Edition),2008,38(4):950-954.(in Chinese)
[22] 行鸿彦,张强,徐伟.混沌海杂波背景下的微弱信号检测混合算法[J].物理学报,2015,64(4):040506. Xing Hong-Yan,Zhang Qiang,Xu Wei.Hybrid algorithm for weak signal detection in chaotic sea clutter[J].Acta Physica Sinica,2015,64(4):040506. (in Chinese)
[23] Takens F.Dynamical systems and turbulence[J].Lecture Notes in Mathematics,1981,898(9):366.
[24] Cao L.Practical method for determining the minimum embedding dimension of a scalar time series[J].Physica D:Nonlinear Phenomena,1997,110(1):43-50.
[25] She D,Yang X.A new adaptive local linear prediction method and its application in hydrological time series[J].Mathematical Problems in Engineering,2010,2010:205438.
[26] Simon Haykin.Adaptive Filter Theory(Fourth Edition)[M].Pearson Education,2003.

基于局域线性法和卡尔曼滤波的强混沌背景下正弦信号恢复的混合算法

LL-P-KF Hybrid Algorithm for Detecting and Recovering Sinusoidal Signal in Strong Chaotic Noise

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	胡振涛, 杨诗博, 胡玉梅, 周林, 金勇, 杨琳琳. 基于变分贝叶斯的分布式融合目标跟踪[J]. 电子学报, 2022, 50(5): 1058-1065.
[2]	王国庆, 杨春雨, 马磊. 基于多变量Laplace分布的非线性系统分布式鲁棒状态估计[J]. 电子学报, 2022, 50(11): 2610-2618.
[3]	徐哈宁, 肖慧, 邓居智. 基于SIMM-UKF和电阻率成像技术的滑坡体形变预测算法研究[J]. 电子学报, 2021, 49(2): 354-361.
[4]	刘剑锋, 孙力帆, 普杰信, 何子述, 王燕玲. 基于刚性约束的双移动机器人协同定位[J]. 电子学报, 2020, 48(9): 1777-1785.
[5]	叶瑾, 许枫, 杨娟, 王佳维. 一种基于多传感器的复合量测IMM-EKF数据融合算法[J]. 电子学报, 2020, 48(12): 2326-2330.
[6]	褚金奎, 武进, 李金山, 支炜. 偏振光传感器在四旋翼飞行器中的应用[J]. 电子学报, 2020, 48(1): 198-203.
[7]	黄锦旺, 吕善翔, 李广明, 袁华强. 基于增殖系数的混沌信号提取算法[J]. 电子学报, 2019, 47(4): 855-861.
[8]	张浩为, 谢军伟, 葛佳昂, 宗彬锋, 路文龙. 基于波形调度的机动目标跟踪算法[J]. 电子学报, 2019, 47(3): 560-567.
[9]	王悦斌, 蒋景飞, 张建秋. 动态出现和/或消失时频信号的模型和分析[J]. 电子学报, 2019, 47(2): 495-501.
[10]	廖勇, 张楠, 姚海梅, 花远肖, 赵砚. 高速场景下基于叠加导频的迭代EKF信道估计方法[J]. 电子学报, 2019, 47(11): 2399-2406.
[11]	周牧, 耿小龙, 谢良波, 聂伟, 田增山. 室内Wi-Fi/PDR自适应鲁棒卡尔曼滤波融合定位方法[J]. 电子学报, 2019, 47(1): 9-15.
[12]	李凡, 熊家军, 李冰洋, 兰旭辉, 喻晨龙, 叶泽浩. 临近空间高超声速跳跃式滑翔目标跟踪模型[J]. 电子学报, 2018, 46(9): 2212-2221.
[13]	陆彩霞, 杨飞然, 杨军. 一种可快速再跟踪的无延时频域卡尔曼滤波啸叫抑制算法[J]. 电子学报, 2018, 46(8): 1954-1959.
[14]	闫雷兵, 陆音, 张业荣. 基于TOA/TDOA的一致性正交容积卡尔曼跟踪算法[J]. 电子学报, 2018, 46(8): 1989-1996.
[15]	王文波, 晋云雨, 王斌, 李维刚, 汪祥莉. 混沌信号的自适应阈值同步挤压小波变换消噪[J]. 电子学报, 2018, 46(7): 1652-1657.