Application of wavelet threshold algorithm optimized by chaotic adaptive fireworks algorithm in the de-noising of muzzle response signals

To extract the muzzle response signals effectively and reduce the noise in the signals, a novel de-noising method, i.e., wavelet threshold algorithm optimized by the chaotic adaptive fireworks algorithm, is proposed for the de-noising of the muzzle response signals. This method obtains the wavelet threshold by calculating the Stein’s unbiased risk estimate (SURE), and uses the chaotic adaptive fireworks algorithm to search for the global optimal threshold, which can avoid the threshold falling into local optimal value effectively. Meanwhile, a novel threshold function is adopted to keep the balance between the signal preservation and noise filtering. To verify the de-noising effect of this method, the wavelet threshold algorithm optimized by gradient descent algorithm and the wavelet threshold algorithm based on standard soft threshold function are introduced to compare the denoising effect of the Blocks signal, HeaviSine signal and the measured muzzle response signal under the interference of Gaussian white noise. The results show that the proposed method can effectively suppress the noise while retaining the details in the muzzle response signal, and exhibits a promising prospect in practical application.