Deep Neural Network-Based Scale Feature Model for BVI Detection and Principal Component Extraction

Abstract

The blade-vortex interaction (BVI) is a typical helicopter noise, and has received significant attentions in the fields of structural stealth and acoustic detection. In this paper, a hybrid scheme combining aerodynamic and acoustic analysis based on the deep neural network (DNN) is proposed to achieve a better understanding of the BVI. Meanwhile, the DNN-based scale feature model (DNN-SFM) is constructed to describe the end-to-end relationship between the aero-acoustic parameters of the BVI signal and the optimal wavelet scale feature by the MZ-discrete wavelet transform. Two novel methods based on DNN-SFM are proposed for the BVI signal detection and principal component extraction, which effectively reduces the time complexity and improves the robustness in a variety of noisy environments compared to traditional algorithms. The extensive experiments on simulated and realistic data verify the effectiveness of our methods.