Analysis of impulse noise based on wavelet transform for military applications

In this project, we present a data acquisition and analysis study of impulse noise based on wavelet transform for military applications. Impulse noise is a type of highly transient signal widely experienced in military fields (e.g., an intense blast wave). The wavelet transform has been used to analyze signals of impact noise and vibrations, and it showed superior advantages on analysis of transient signals compared to the fast Fourier transform and the short-time Fourier transform. This study focuses on analysis of A-wave type impulse noise in the T-F domain using the continual wavelet transforms. Three different wavelets (i.e., Morlet, Mexican hat, and Meyer wavelets) were investigated and compared based on theoretical analysis and applications to experimental generated impulse noise signals. The underlying theory of continual wavelet transform was given and the temporal and spectral resolutions of different wavelets were theoretically analyzed. The results on singularity detection of the impulse noise showed the Mexican hat wavelet could better reflect the signal oscillations. Furthermore, the similarity of signals between the impulse noise and wavelets functions was investigated in time and frequency domain. The results showed the waveform of Mexican hat wavelets is more similar to the impulse noise signal than the other two wavelets. In summary, although all of three wavelets can represent detailed features of impulse noise in the T-F domain, the Mexican hat wavelets show obvious advantages over the Morlet and Meyer wavelets. The results of this study provide a possible strategy to design special wavelets for impulse noise detection and analysis.