探测器
连贯性(哲学赌博策略)
稳健性(进化)
工程类
空气动力学
声学
话筒
噪音(视频)
近场和远场
麦克风阵列
噪声测量
计算机科学
航空航天工程
物理
人工智能
降噪
电气工程
光学
生物化学
化学
声压
量子力学
图像(数学)
基因
作者
Liang Yu,Longjing Yu,Ran Wang,Chunhua Wei,Kevin Renheng Xu,Rui Wang
标识
DOI:10.1016/j.ymssp.2022.109754
摘要
Helicopter detection is an important part of the safety and security during flight. Most of the aerodynamic noise of modern helicopters comes from the main rotor, the main rotor aerodynamic noise thus can be used for helicopter detection. However, the Signal-to-Noise Ratio (SNR) of the measurement noise has always been quite small while the helicopter is flying over long distance away from the microphone. The current passive detection methods are not robust enough in the presence of substantial interference. A robust passive sound detection method is proposed to detect the far-field helicopter based on the cyclostationarity of the main rotor noise. The Sparsity-Enhanced Spectral Coherence (SESC) is derived from the spectral coherence decomposition to improve the detection robustness at far distances and low SNR. Furthermore, a global detector is constructed to detect helicopters adaptively by fusing the detection of multiple orders of BPF, which can be performed on the calculated SESC. More accurate and robust detection of the far-field helicopter can be obtained by the proposed SESC detector. The effectiveness of the proposed detection approach is demonstrated and contrasted by using simulation and far-field flight test measurements of the ROBINSON R22 helicopter.
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