A precise feature extraction method for shock wave signal with improved CEEMD-HHT

Abstract

Efficient extraction of feature parameters is the key to evaluating weapon damage performance. At present, many classical feature extraction algorithms have the problem that the extraction cannot meet the actual needs. A precise feature extraction method based on improved complementary ensemble empirical mode decomposition (CEEMD) with Hilbert-Huang Transform (HHT) was proposed in this paper to solve problems such as large noise and difficulties in extracting features of shockwave overpressure signals in complex test environment. We introduced CEEMD to decompose original explosion shockwave signals and adopted wavelet packet threshold de-noising to extract useful information from noisy high-frequency intrinsic mode functions (IMFs). The correlation coefficient algorithm is introduced to remove unrelated IMFs. In addition, we performed reconstruction of original signals to extract true time-course feature and utilized Hilbert-Huang Transform (HHT) to achieve precise extraction of instantaneous feature and energy spectrum of the various IMFs. The improved CEEMD-HHT is a precise method for shock wave signal analysis. It not only effectively removes noise, but also retains effective high-frequency information without losing useful information. Additionally, it overcomes the problems of mode mixing in empirical mode decomposition (EMD), and has the advantages of feature extraction with high accuracy and self-adaptation. The effectiveness of the proposed method is demonstrated by 2 groups of experimental data, and it precisely extracts instantaneous feature and energy spectrum of shockwave overpressure signal, which provide new theoretical basis for the evaluation of weapon damage.