ZHANG Jian-sheng, LIN Shu-yu, MIAO Run-cai, YANG Wan-min. One-dimensional wavelet transformation analysis on specialty of scattered light from ship wakes[J]. Journal of Applied Optics, 2007, 28(3): 245-249.
Citation: ZHANG Jian-sheng, LIN Shu-yu, MIAO Run-cai, YANG Wan-min. One-dimensional wavelet transformation analysis on specialty of scattered light from ship wakes[J]. Journal of Applied Optics, 2007, 28(3): 245-249.

One-dimensional wavelet transformation analysis on specialty of scattered light from ship wakes

  • Ship wakes have special acoustic, electromagnetic and optical characteristics. Onedimensional wavelet transformations, including dispersive wavelet, continuous wavelet, wavelet packet and complex continuous wavelet transformations, are used to analyze the scattered optical specialty of ship wakes (SOSSW). SOSSW under different conditions have distinct autothresholds, different retained energy and zero setting ratio of the wavelet coefficient. The coloration modes, coefficient curves, and local maximal coefficient lines vary distinctly. The onedimensional disperse wavelet transformation reveals that the detailed signal holds particular periodicity. The analysis of the wavelet detailed semaphore demonstrates the way to eliminate or decrease the effects of these factors. Wavelet coefficients, its distributions and maximal lines differentiate obviously under different pressures according to the onedimensional continuous wavelet transformation. The compression signal of the onedimensional wavelet packet transformation retains the major energy of the original signal. The selected coefficients can act as a characteristic coefficient of the wake bubble curtain under different pressures. The relative magnitude of the mode, mode angle and coefficient distribution along time axis differ from each other based on the onedimensional complex continuous wavelet transformation. The common characteristics of the scattered light signal can be extracted and the difference of various scattered light signals can be directly observed through wavelet analysis, which is useful in wake identification.
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