Transient signal detection using the empirical mode decomposition

Michael L. Larsen; Jeffrey Ridgway; Cye H. Waldman; Michael Gabbay; Rodney R. Buntzen; Brad Battista

doi:10.1117/12.561301

26 October 2004 Transient signal detection using the empirical mode decomposition

Michael L. Larsen, Jeffrey Ridgway, Cye H. Waldman, Michael Gabbay, Rodney R. Buntzen, Brad Battista

Proceedings Volume 5559, Advanced Signal Processing Algorithms, Architectures, and Implementations XIV; (2004) https://doi.org/10.1117/12.561301
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

Abstract

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.

Citation Download Citation

Michael L. Larsen, Jeffrey Ridgway, Cye H. Waldman, Michael Gabbay, Rodney R. Buntzen, and Brad Battista "Transient signal detection using the empirical mode decomposition", Proc. SPIE 5559, Advanced Signal Processing Algorithms, Architectures, and Implementations XIV, (26 October 2004); https://doi.org/10.1117/12.561301

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