NON-STATIONARY DECOMPOSITION USING THE DISCRETE LINEAR CHIRP TRANSFORM (DLCT) FOR FM DEMODULATION

被引:0
作者
Hari, A. [1 ]
Alkishriwo, O. A. [1 ]
Chaparro, L. F. [1 ]
Akan, Aydin [2 ]
机构
[1] Univ Pittsburgh, Dept Elect & Comp Engn, Pittsburgh, PA 15261 USA
[2] Istanbul Univ, Dept Elect & Elect Eng, Istanbul, Turkey
来源
2013 PROCEEDINGS OF THE 21ST EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | 2013年
关键词
EMD; DLCT; FM demodulation; Hilbert-Huang spectrum; EMPIRICAL MODE DECOMPOSITION; SPECTRUM;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, we consider FM demodulation as an application of the decomposition of non-stationary signals. Non-stationary signal decomposition can be done using either the empirical mode decomposition (EMD) or the Discrete Linear Chirp Decomposition (DLCT) methods. These methods decompose non-stationary signals using local time-scale signal characteristics. While the EMD decomposes the signal into a number of intrinsic mode functions (IMFs), the DLCT obtains a parametric model based on a local linear chirp model. Analytically the DLCT considers localized zero-mean linear chirps as special IMFs. The DLCT is a joint frequency instantaneous-frequency orthogonal transformation that extends the discrete Fourier transform (DFT) for processing of non-stationary signals. FM demodulation is commonly done by computing the signal derivative to convert it into an amplitude demodulation. We will show that the demodulation can be approached with the EMD and the DLCT and that the second method provides better results. The performance of the DLCT and the EMD are illustrated and compared when used as an FM demodulation scheme in software defined radio.
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共 12 条
  • [1] [Anonymous], P IEEE INT C INF SCI
  • [2] [Anonymous], 2012, IEEE T SIGNAL PROCES
  • [3] [Anonymous], 2012, P IEEE CUST INT CIRC
  • [4] Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble Empirical Mode Decomposition
    Chang, Kang-Ming
    Liu, Shing-Hong
    [J]. JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY, 2011, 64 (02): : 249 - 264
  • [5] Empirical mode decomposition as a filter bank
    Flandrin, P
    Rilling, G
    Gonçalvés, P
    [J]. IEEE SIGNAL PROCESSING LETTERS, 2004, 11 (02) : 112 - 114
  • [6] The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
    Huang, NE
    Shen, Z
    Long, SR
    Wu, MLC
    Shih, HH
    Zheng, QN
    Yen, NC
    Tung, CC
    Liu, HH
    [J]. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 1998, 454 (1971): : 903 - 995
  • [7] Hilbert-Huang Transform for Analysis of Heart Rate Variability in Cardiac Health
    Li, Helong
    Kwong, Sam
    Yang, Lihua
    Huang, Daren
    Xiao, Dongping
    [J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2011, 8 (06) : 1557 - 1567
  • [8] Li X., 2011, PROC IEEE INTERL WOR, P1
  • [9] Single-mixture audio source separation by subspace decomposition of Hilbert spectrum
    Molla, Md. Khademul Islam
    Hirose, Keikichi
    [J]. IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2007, 15 (03): : 893 - 900
  • [10] Time-Frequency Analysis of EEG Asymmetry Using Bivariate Empirical Mode Decomposition
    Park, Cheolsoo
    Looney, David
    Kidmose, Preben
    Ungstrup, Michael
    Mandic, Danilo P.
    [J]. IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2011, 19 (04) : 366 - 373