Adaptive cone of influence-based blade-vortex interference signal detection. (August 2022) - Wang, Lu; Bao, Ming; Zhao, Qijun; Zhao, Jing; Wei, Chunhua
Highlights: Define the COI feature to capture the dominant information of a BVI signal. Design the FCNN-based A-COI model to adaptively acquire the COI features. Derive a generalized likelihood ratio-based detection algorithm under the low SNR. Abstract: An adaptive cone of influence (A-COI)-based method in the form of a full convolutional neural network (FCNN) is proposed, which leverages the principle of edge detection to detect the blade-vortex interference (BVI) signal of a helicopter under low signal-to-noise ratio. First, the COI feature is defined, which captures the dominant information of a BVI signal based on the time-scale diagram obtained through the continuous wavelet transform. Then, the FCNN-based A-COI model is designed and trained, which can conduct edge detection for the time-scale diagram and then adaptively acquire the COI features. Finally, with the obtained COI feature, a model of BVI signal representation is constructed and then a generalized likelihood ratio-based detection algorithm in the wavelet domain is derived to detect the BVI signal under low signal-to-noise ratio. The simulated and measured data-based experimental results demonstrate that the proposed method can effectively improve the performance of detecting BVI signals under the low signal-to-noise ratio.