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In order to overcome the robustness of low altitude passive acoustic target recognition, the manifold learning was originally introduced to the feature extraction of acoustic targets. Based on the classical algorithm of manifold learning, the paper studied and discussed the low dimensional manifold in the frequency-domain of acoustic signals. This method was applied to acoustic target recognition...
The focus of this paper is to achieve robust recognition of low altitude flying targets in diverse battlefield environmental through extracting their acoustic features. A new feature called SB-RC was proposed based on WPT and RCC in this paper. Allowing variable time-frequency resolution and being immune to noise, WPT was employed to replace FFT in the computation of MFCC to overcome its sensitivity...
An approach is proposed to classifying simultaneous multiple low altitude targets in battlefield. Based on Independent Component Analysis (ICA), the mixed signal is separated into several single and pure signals, and the noise is removed from the acoustic signal. mel-frequency cepstrum coefficients (MFCC) which responses the characteristic of the sound more aggressively is extracted as characteristic...
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