Abstract
This paper proposes a method for estimating the instantaneous frequency of a nonstationary signal; this method is based on a combination of empirical mode decomposition and functional data analysis. The proposed method incorporates a basis expansion technique for a functional data into time-varying phase derived by empirical mode decomposition and Hilbert transform, which provides a stable instantaneous frequency function. The superiority of the proposed method for instantaneous frequency estimation is demonstrated by various simulation studies. The analysis of multicomponent signals by the proposed method is also discussed. Furthermore, it is shown that the proposed method is highly effective for identifying groups (clusters) of nonstationary signals on the basis of the instantaneous frequency information.













Similar content being viewed by others
References
Boashash B (1992a) Estimating and interpreting the instantaneous frequency of a signal-part 1: fundamentals. Proc IEEE 80:519–538
Boashash B (1992b) Estimating and interpreting the instantaneous frequency of a signal-part 2: algorithms and applications. Proc IEEE 80:540–567
Boashash B, White LB (1990) Instantaneous frequency estimation and automatic time-varying filtering. ICASSP 3–6:1221–1224
Carmona R, Hwang WL, Torrésani B (1997) Characterization of signals by the ridges of their wavelet transforms. IEEE Trans Signal Process 45:2586–2590
Carmona R, Hwang WL, Torrésani B (1998) Practical time-frequency analysis. Academic Press, San Diego
Craven P, Wahba G (1978) Smoothing noisy data with spline functions. Numerische Mathematik 31(4):377–403
De Boor C (1978) A practical guide to splines. Springer, New York
Gabor D (1946) Theory of communication. Proc IEE 93(III):429–457
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, 2nd edn. Springer, New York
Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc R Soc Lond A 454:903–995
Kopsinisa Y, Aboutanios E, Waters DA, McLaughlin S (2010) Time-frequency and advanced frequency estimation techniques for the investigation of bat echolocation calls. J Acoust Soc Am 127(2):1124–1134
Ramsay JO, Silverman BW (2002) Applied functional data analysis. Springer, New York
Ramsay JO, Silverman BW (2005) Functional data analysis, 2nd edn. Springer, New York
Rao AR, Hsu E-C (2008) Hilbert-Huang transform analysis of hydrological and environmental time series. Springer, New York
Rilling G, Flandrin P (2008) One or two frequencies ? The empirical mode decomposition answers. IEEE Trans Signal Process 56(1):85–95
Tichavský P, Händel P (1999) Multicomponent polynomial phase signal analysis using a tracking algorithm. IEEE Trans Signal Process 47(5):1390–1395
Tretter SA (1985) Estimating the frequency of a noisy sinusoid by linear regression. IEEE Trans Inf Theory 31(6):832–835
Ville J (1948) Theorie et application de la notion de signal analytic. Cables et Transmissions 2A(1):61–74, Paris, France. (trans: Selin I). Theory and applications of the notion of complex signal. Report T-92, RAND Corporation, Santa Monica, CA
Wand MP, Jones MC (1995) Kernel smoothing. Chapman & Hall/CRC, New York
Wahba G (1990) Spline models for observational data. SIAM, Philadelphia
Acknowledgments
This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0030811).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Park, M., Cho, S. & Oh, HS. The role of functional data analysis for instantaneous frequency estimation. Comput Stat 28, 1965–1987 (2013). https://doi.org/10.1007/s00180-012-0389-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00180-012-0389-y