脉冲声信号影响锥特征的自适应选取EI北大核心CSCD

为了解决低信噪比下脉冲声信号影响锥特征的自适应选取和检测问题,提出了一种改进的整体嵌套边缘检测方法。利用脉冲信号小波域的时间-尺度分析谱图中明显的边缘效应特征,构造自适应影响锥(A-COI)模型。该模型可自适应输出最适影响锥部分,在减弱噪声干扰的同时最大程度的包含了脉冲信号的主要特征。进而将最适影响锥部分对应的小波系数用于脉冲信号检测,有效提升了低信噪比下的检测性能。对典型直升机桨-涡干扰脉冲信号的仿真和实验数据进行分析,结果表明在0 dB,2 dB,5 dB信噪比的复杂环境下,使用基于A-COI模型的检测率分别达到了65.13%,82.33%,95.27%,相对于传统固定大小影响锥的检测算法提升了42.42%,22.99%和2.36%。

Adaptively selecting the cone of influence features of impulsive acoustic signals

An improved holistically-nested edge detection method is proposed to solve the problem of adaptively selecting and detecting the cone of influence features of impulsive acoustic signals under low Signal-to-Noise Ratio (SNR). By leveraging the significant edge effects in the time-scale analysis spectrum obtained by the wavelet transform for the impulsive signal, an Adaptive Cone of Influence (A-COI) model is constructed. The model can adaptively output the most suitable area in the cone of influence, which contains the main features of the impulsive signal to the greatest extent while reducing the noise interference. The wavelet coefficient corresponding to the optimum cone of influence is used to assist the design of the impulsive signal detector, and effectively improves the detection performance under low SNR. The simulation and experimental data of helicopter blade-vortex interference signal (a typical helicopter impulsive signal) are analyzed. The results verify that the results show that under the complex environments with SNR of 0 dB, 2 dB and 5 dB, the detection rate based on the A-COI model reaches 65.13%, 82.33% and 95.27%, which compared with the traditional fixed-size impact cone detection algorithm, the improvement is 42.42%, 22.99% and 2.36% respectively.