Abstract
Due to the randomness of added noise, noise-assisted versions based on EMD (empirical mode decomposition) usually cause new “mode mixing” problem. In addition, these algorithms also have problems such as high time-consuming and large recovering error. For the reasons, a new method SN-EMD (Selective Noise-assisted EMD) is put forward in this paper. It determines whether to add noise as assistance by judging whether there is high frequency intermittent component contained in the signal or not. The new method was proved to have the optimal performance by comparing the performance parameters for evaluating the decomposition. In this paper, SN-EMD was used to decompose ship radiated noise. On account of the differences in the original information contained in each mode of radiated noise signals from different ship, we selected the first three modes for processing. Average instantaneous frequency, center frequency, energy density, and energy distribution ratio were extracted as mode feature of ship targets for classification and recognition. Spatial distribution of the feature quantities in three-dimensional space verified similarity of the same target and separability of different targets.
Highlights
EMD [1] is an adaptive signal processing method proposed by N
The improved CEEMDAN still has aspects for improvement: (I) time-consuming issues: in order to reduce the residual error, it needs hundreds of decomposition over an ensemble of noisy copies of the original signal, so the algorithm is time-consuming; (II) the problem of mode mixing: for the reason of added noise, this algorithm can avoid decomposing the components on different time scales into the same mode, but there is still a second type of mode mixing phenomenon that the components at the same time scale are decomposed into different modes
Based on the adaptive mode decomposition, the mode decomposition of ship radiated noise and mode feature extraction have been studied in this paper
Summary
EMD [1] is an adaptive signal processing method proposed by N. In order to solve the problem of mode mixing, based on the EMD algorithm, a series of noise-assisted algorithms named Ensemble Empirical Mode Decomposition (EEMD) [2], Complementary Ensemble Empirical Mode Decomposition (CEEMD) [3], Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) [4], and improved CEEMDAN [5] were proposed consecutively. The improved CEEMDAN still has aspects for improvement: (I) time-consuming issues: in order to reduce the residual error, it needs hundreds of decomposition over an ensemble of noisy copies of the original signal, so the algorithm is time-consuming; (II) the problem of mode mixing: for the reason of added noise, this algorithm can avoid decomposing the components on different time scales into the same mode, but there is still a second type of mode mixing phenomenon that the components at the same time scale are decomposed into different modes.
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