动态出现和/或消失时频信号的模型和分析

doi:10.3969/j.issn.0372-2112.2019.02.034

电子学报 ›› 2019, Vol. 47 ›› Issue (2): 495-501.DOI: 10.3969/j.issn.0372-2112.2019.02.034

动态出现和/或消失时频信号的模型和分析

王悦斌^1,2, 蒋景飞², 张建秋^1,2

1. 复旦大学智慧网络系统研究中心和电子工程系, 上海 200433;
2. 电子信息控制重点实验室, 四川成都 610036

收稿日期:2017-12-13 修回日期:2018-04-10 出版日期:2019-02-25
- 作者简介:
- 王悦斌 男,1993年5月出生,山东济宁人.2012年毕业于苏州大学通信工程系,取得理学学士学位.现为复旦大学电子工程系在读硕士生,主要从事时频分析、多目标追踪等方面的研究.E-mail:yuebinwang16@fudan.edu.cn;蒋景飞 男,1985年9月出生于四川省西昌市分别于2008年和2011年在复旦大学电子工程系获得理学学士和硕士学位.现为中国电子科技集团第二十九研究所工程师,主要研究方向为阵列信号处理.E-mail:082021030@fudan.edu.cn
- 基金资助:
- 国家自然科学基金 (No.61571131）; 电子信息控制重点实验室基金

A Time-Frequency Model and Analytical Method for Multiple Modulated Components with Dynamic Births and Deaths

WANG Yue-bin^1,2, JIANG Jing-fei², ZHANG Jian-qiu^1,2

1. The Research Center of Smart Networks and Systems and Department of Electronic Engineering, Fudan University, Shanghai 200433, China;
2. Science and Technology on Electronic Information and Control Laboratory, Chengdu, Sichuan 610036, China

Received:2017-12-13 Revised:2018-04-10 Online:2019-02-25 Published:2019-02-25

摘要/Abstract

摘要： 针对动态出现和/或消失的时频信号，本文提出了一种时频模型和分析方法.该模型以时频信号各分量的幅度和相位为状态变量，并利用多项式预测模型为它们建立起状态方程，再视多分量混合时频信号的观测为测量方程，这就为多分量时频信号建立了状态空间模型.为了应对时频信号分量动态出现和/或消失的情况，本文利用非参数迭代自适应谱估计方法对时间加窗的信号进行分析，得到该时间窗内的短时谱，以该短时谱中噪声谱的3倍标准差准则来确定信号分量的数目.由此，基于提出的模型，就可利用无迹卡尔曼滤波算法来分析动态出现和/或消失的时频信号.分析和仿真均表明：提出方法无论在精确度、分辨率以及交叉时频谱分离等方面均优于文献报道的方法.

关键词: 动态出现和/或消失, 多项式预测模型, 迭代自适应谱估计, 无迹卡尔曼滤波

Abstract: In this paper,a new time-frequency model and analytical method are proposed for multiple modulated components with dynamic births and deaths.The proposed model takes the amplitude and phase of components as the state variables whose evolutions are described as polynomial prediction models.The observations of the signal are then viewed as the measurements of the states.In this way,a new state space model is built to describe the signal with multiple modulated components.In order to cope with the dynamic births and deaths of the components,a non-parametric iterative adaptive approach with a time window is employed to obtain the short time spectrums.The number of signal components is found under the rule of three times standard deviation of the noise in the spectrums.Now,the unscented Kalman filter can be used to analyze the dynamic births and deaths signals based on the proposed model.Simulation results verify the effectiveness of the proposed method while its performances shown in terms of accuracy,resolution and crossing spectrum separation are much better than the ones reported in literature.

Key words: dynamic births and deaths, polynomial prediction model, iterative adaptive approach, unscented Kalman filter

中图分类号:

TN911.7

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.

WANG Yue-bin, JIANG Jing-fei, ZHANG Jian-qiu. A Time-Frequency Model and Analytical Method for Multiple Modulated Components with Dynamic Births and Deaths[J]. Acta Electronica Sinica, 2019, 47(2): 495-501.

参考文献

[1] STANKOVIC L J,THAYAPARAN T,DAKOVIC M.Signal decomposition by using the S-method with application to the analysis of HF radar signals in sea-clutter[J].IEEE Transactions on Signal Processing,2006,54(11):4332-4342.
[2] STANKOVIC L,STANKOVIC S,THAYAPARAN T,et al.Separation and reconstruction of the rigid body and micro-Doppler signal in ISAR part I-theory[J].Radar Sonar & Navigation IET,2015,9(9):1147-1154.
[3] UMAPATHY K,KRISHNAN S,JIMAA S.Multigroup classification of audio signals using time-frequency parameters[J].IEEE Transactions on Multimedia,2005,7(2):308-315.
[4] BENTLEY P M,GRANT P M,MCDONNELL J T E.Time-frequency and time-scale techniques for the classification of native and bioprosthetic heart valve sounds[J].IEEE Transactions on Bio-Medical Engineering,1998,45(1):125-128.
[5] Wigner E.On the quantum correction for thermodynamic equilibrium[J].Physical Review,1932,40(5):749-759.
[6] STOCKWELL R G,MANSINHA L,LOWE R P.Localization of the complex spectrum:The s transform[J].IEEE Transactions on Signal Processing,1996,44(4):998-1001.
[7] Li Z Y,MARTIN N.A time-frequency based method for the detection and tracking of multiple non-linearly modulated components with births and deaths[J].IEEE Transactions on Signal Processing,2016,64(5):1132-1146.
[8] ZHONG J,HUANG Y.Time-frequency representation based on an adaptive short-time Fourier transform[J].IEEE Transactions on Signal Processing,2010,58(10):5118-5128.
[9] WAGHMARE R G,SUBRAHMANYAM S,MISHRA D.Signal tracking approach for simultaneous estimation of phase and instantaneous frequency[A].IEEE International Conference on Signal Processing,Informatics,Communication and Energy Systems[C].IEEE,2015.1-5.
[10] CAREVIC D,DAVEY S.Two algorithms for modeling and tracking of dynamic time-frequency spectra[J].IEEE Transactions on Signal Processing,2016,64(22):6030-6045.
[11] HEINONEN P,NEUVO Y.FIR-median hybrid filters with predictive FIR substructures[J].IEEE Transactions on Acoustics Speech & Signal Processing,2002,36(6):892-899.
[12] STOICA P,LI J,HE H.Spectral analysis of nonuniformly sampled data:A new approach versus the periodogram[J].IEEE Transactions on Signal Processing,2009,57(3):843-858.
[13] ZHANG J Q.An eigenvalue residuum-based criterion for detection of the number of sinusoids in white Gaussian noise[J].Digital Signal Processing,2003,13(2):275-283.
[14] JULIER S J,UHLMANN J K.Unscented filtering and nonlinear estimation[J].Proceedings of the IEEE,2004,92(3):401-422.
[15] WANG S,CHEN X,CAI G,et al.Matching demodulation transform and synchrosqueezing in time-frequency analysis[J].IEEE Transactions on Signal Processing,2014,62(1):69-84.
[16] STONE M H.The generalized weierstrass approximation theorem[J].Mathematics Magazine,1948,21(4):167-184.
[17] PEARSON K.The problem of the random walk[J].Nature,1905,72(1865):294.
[18] XU L,ZHANG J Q,YAN Y.A wavelet-based multisensor data fusion algorithm[J].IEEE Transactions on Instrumentation & Measurement,2004,53(6):1539-1545.
[19] RISTIC B,ARULAMPALAM S,GORDON N.Beyond the Kalman filter:Particle filters for tracking applications[J].IEEE Trans of Aerospace & Electronic Systems,2003,19(7):37-38.
[20] SCHUHMACHER D,VO B T,VO B N.A consistent metric for performance evaluation of multi-object filters[J].IEEE Transactions on Signal Processing,2008,56(8):3447-3457.

动态出现和/或消失时频信号的模型和分析

A Time-Frequency Model and Analytical Method for Multiple Modulated Components with Dynamic Births and Deaths

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

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]	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.
[3]	王磊, 程向红, 李双喜. 高斯和高阶无迹卡尔曼滤波算法[J]. 电子学报, 2017, 45(2): 424-430.
[4]	张玲霞, 刘志仓, 王辉, 齐会云, 胡旦. 非线性系统故障诊断的粒子滤波方法[J]. 电子学报, 2015, 43(3): 615-619.
[5]	商临峰, 杨小军, 邢科义. 局域跟踪的测速雷达网量测融合弹道解算[J]. 电子学报, 2013, 41(3): 615-618.
[6]	曲彦文, 张二华, 杨静宇. 交互式多区域模型[J]. 电子学报, 2012, 40(6): 1235-1239.