摘要
首先分析了空气动力性噪声信号的混沌特性及其非平稳性,然后分析统计了坦克、直升机声信号的最大LyaPunov指数λ1、分维数DB和不同尺度小波子空间的能量分布特征。最后,利用这些特征矢量和BP神经网络对实测的坦克和直升机数据进行了分类。结果表明在不同信噪比情形下都取得了令人满意的分类正确率。
Firstly, the chaos performance and the nature of nonstationary of the aerodynamic noise arediscussed. Then, the distributions of the maximum Lyapunov exponent λ1, the fractal dimension andthe energy distribution indifferent wavelet scale space of the noise of the tank and helicopter are testedand analyzed statistically. Finally, these feature vectors and BP neural network are used to classifythe realistic data of tank and helicopter. Results show that the classification system can achieve astatisfactory classification accuracy in different SNR.
出处
《声学学报》
EI
CSCD
北大核心
1999年第2期197-203,共7页
Acta Acustica
关键词
混沌
分形
小波理论
被动声信号
特征提取
Acoustic noise
Chaos theory
Fractals
Neural networks
Wavelet transforms