Transient signal detection using the empirical mode decomposition

In this paper, we report on efforts to develop signal processing methods appropriate for the detection of man-made electromagnetic signals in the nonlinear and nonstationary underwater electromagnetic noise environment of the littoral. Using recent advances in time series analysis methods [Huang et al., 1998], we present new techniques for detection and compare their effectiveness with conventional signal processing methods, using experimental data from recent field experiments. These techniques are based on an empirical mode decomposition which is used to isolate signals to be detected from noise without a priori assumptions. The decomposition generates a physically motivated basis for the data.

[1]  Thomas B. Sanford,et al.  Motionally Induced Electric and Magnetic Fields in the Sea , 1971 .

[2]  A. Quinquis,et al.  Using the wavelet transform for the detection of magnetic underwater transient signals , 1994, Proceedings of OCEANS'94.

[3]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[4]  Paulo Gonçalves,et al.  Empirical Mode Decompositions as Data-Driven Wavelet-like Expansions , 2004, Int. J. Wavelets Multiresolution Inf. Process..

[5]  Roland Blanpain,et al.  Short delay detection of a transient in additive Gaussian noise via higher order statistic test , 1996, 1996 IEEE Digital Signal Processing Workshop Proceedings.

[6]  J. Berthier,et al.  Geomagnetic noise reduction from sea surface, high sensitivity magnetic signals using horizontal and vertical gradients , 1998, IEEE Oceanic Engineering Society. OCEANS'98. Conference Proceedings (Cat. No.98CH36259).

[7]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[8]  J. Clarke,et al.  Signals and Noise in Measurements of Low‐Frequency Geomagnetic Fields , 1988 .

[9]  Gabriel Rilling,et al.  Detrending and denoising with empirical mode decompositions , 2004, 2004 12th European Signal Processing Conference.

[10]  S. Webb,et al.  Pressure and electric fluctuations on the deep seafloor: Background noise for seismic detection , 1984 .

[11]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[12]  Bruce M. Lake,et al.  Nonlinear Dynamics of Deep-Water Gravity Waves , 1982 .

[13]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[14]  Patrick Flandrin,et al.  Sur la décomposition modale empirique , 2003 .

[15]  B. Deniel Undersea magnetic noise reduction , 1997, Oceans '97. MTS/IEEE Conference Proceedings.

[16]  A. Quinquis,et al.  Detection of underwater magnetic transient signals by higher order analysis , 1995, 'Challenges of Our Changing Global Environment'. Conference Proceedings. OCEANS '95 MTS/IEEE.

[17]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[18]  M. Ochi Ocean Waves: The Stochastic Approach , 1998 .

[19]  K. Hasselmann On the non-linear energy transfer in a gravity-wave spectrum Part 1. General theory , 1962, Journal of Fluid Mechanics.

[20]  K. Hasselmann On the non-linear energy transfer in a gravity wave spectrum Part 2. Conservation theorems; wave-particle analogy; irrevesibility , 1963, Journal of Fluid Mechanics.

[21]  H. W. Smith,et al.  Magnetic Anomaly Detection Utilizing Component Differencing Techniques , 1973 .