摘要
近年来,低空飞行声目标的探测与识别已得到军事领域的重点关注,而如何滤除信号中的背景噪声并准确保留信号的有效特征信息是该领域的一个难点。在研究小波去噪算法特点的基础上,针对低空飞行声目标信号的噪声特性,构建了一个新的阈值函数,通过自适应调整阈值函数实现在小波分解细尺度和宽尺度上对噪声信号最大限度的滤除,同时,运用香农熵理论来判断最优层数。通过大量的实验仿真验证,并与传统阈值去噪算法比较分析,结果表明该算法对去噪指标SNR有较大尺度的提高,可以更好的去除噪声,并对低空声目标信号去噪有很好的去噪效果。
In recent years, detection and recognition of low altitude flying acoustic targets receive attentions in the field of military. How to filter out background noise and preserve characteristics of signal information accurately is the key point in this field. Here, based on studying characteristics of existing wavelet de-noising algorithms, aiming at characteristics of low altitude flying acoustic target signals, a new adaptive wavelet threshold function was put forward. With this function, the noise in the acoustic target's signals was filtered as much as possible on the thin scale and the wide scale of wavelet decomposition. In addition, the theory of Shannon entropy was used to estimate the optimal decomposition level. The simulation results demonstrated that the proposed method can improve the signal-to-noise ratio (SNR) better than the traditional threshold denoising method can; it has a fine de-noising effect on low altitude flying acoustic target signals. © 2017, Editorial Office of Journal of Vibration and Shock. All right reserved.
出处
《振动与冲击》
EI
CSCD
北大核心
2017年第9期153-158,230,共7页
Journal of Vibration and Shock
基金
国家自然科学基金(61663024)
关键词
小波去噪
阈值函数
最优层数
声目标
Acoustic noise
Shrinkage
Signal denoising
Signal processing
Wavelet decomposition
