Explosion Shock Waves Arrangement Entropy Time-varying Peaks Denoising Algorithm of Integrated Decomposition
The measured explosion shock wave signal contains a large amount of noise signals,which seriously affects the interpretation of shock wave overpressure peak and positive pressure time,as well as the calculation of specific impulse.An explosion shock wave denoising algorithm based on fully integrated empirical mode de-composition and arrangement entropy for time-varying window length time-frequency peak filtering(hereinafter referred to as the ensemble decomposition based arrangement entropy time-varying peak explosion shock wave denoising algorithm)e was proposed in this article,which was studied and validated by constructing noisy shock wave signal models and measured data at different proportional distances.The original explosion shock wave da-ta was decomposed into several intrinsic mode components(IMFs)by CEEMDAN;By using the MPE value of IMFs as the classification index,the IMFs components were divided into two categories that require filtering and retention.Noise reduction experiments were conducted on noisy models and measured data,and the denoised IMFs components and remaining IMFs were reconstructed.The experimental results showed that compared with Bessel low-pass digital filter and CEEMDAN denoising algorithm,the method could remove high-frequency noise contained in the signal and achieve better denoising performance;simultaneously preserve the peak and mutation information in the signal as much as possible.
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