本文選題：小波去噪　切入點(diǎn)：閾值函數(shù)　出處：《振動與沖擊》2017年09期 　論文類型：期刊論文

【摘要】：近年來,低空飛行聲目標(biāo)的探測與識別已得到軍事領(lǐng)域的重點(diǎn)關(guān)注,而如何濾除信號中的背景噪聲并準(zhǔn)確保留信號的有效特征信息是該領(lǐng)域的一個難點(diǎn)。在研究小波去噪算法特點(diǎn)的基礎(chǔ)上,針對低空飛行聲目標(biāo)信號的噪聲特性,構(gòu)建了一個新的閾值函數(shù),通過自適應(yīng)調(diào)整閾值函數(shù)實(shí)現(xiàn)在小波分解細(xì)尺度和寬尺度上對噪聲信號最大限度的濾除,同時,運(yùn)用香農(nóng)熵理論來判斷最優(yōu)層數(shù)。通過大量的實(shí)驗(yàn)仿真驗(yàn)證,并與傳統(tǒng)閾值去噪算法比較分析,結(jié)果表明該算法對去噪指標(biāo)SNR有較大尺度的提高,可以更好的去除噪聲,并對低空聲目標(biāo)信號去噪有很好的去噪效果。
[Abstract]:In recent years, the detection and recognition of low altitude acoustic targets has been paid more and more attention in the military field. However, how to filter the background noise from the signal and accurately retain the effective characteristic information of the signal is a difficulty in this field. Based on the study of the characteristics of the wavelet denoising algorithm, the noise characteristics of the low-altitude flight acoustic target signal are studied. A new threshold function is constructed, which adaptively adjusts the threshold function to maximize the filtering of the noise signal on the wavelet decomposition scale and the wide scale. At the same time, The Shannon entropy theory is used to determine the optimal number of layers. A large number of experiments are carried out and compared with the traditional threshold denoising algorithm. The results show that the algorithm can improve the denoising index SNR in a large scale and can remove noise better. And it has good denoising effect for low altitude acoustic target signal.
【作者單位】： 蘭州理工大學(xué)電氣工程與信息工程學(xué)院;蘭州理工大學(xué)理學(xué)院;95876部隊(duì);
【基金】：國家自然科學(xué)基金(61663024)
【分類號】：TN911.4
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本文編號：1645091