本文選題：非下采樣小波包(NWP) + 脈沖星信號(hào)　； 參考：《光電子·激光》2017年06期

【摘要】：為了提高脈沖星信號(hào)的去噪效果,提出了一種基于非下采樣小波包(NWP)分解的局部Laplace模型消噪方法。首先對(duì)真實(shí)脈沖星信號(hào)進(jìn)行NWP分解,統(tǒng)計(jì)真實(shí)脈沖星信號(hào)NWP系數(shù)的分布特性,建立真實(shí)脈沖星信號(hào)小波包系數(shù)的Laplace分布模型;然后在Laplace先驗(yàn)概率分布的基礎(chǔ)上,根據(jù)最大后驗(yàn)概率(MAP)估計(jì)準(zhǔn)則,利用含噪脈沖星信號(hào)的小波包系數(shù)對(duì)真實(shí)脈沖星信號(hào)的小波包系數(shù)進(jìn)行有效估算;最后對(duì)估算出的小波包系數(shù)進(jìn)行NWP重構(gòu),得到消噪后的脈沖星信號(hào)。采用不同的脈沖星信號(hào)進(jìn)行實(shí)驗(yàn)分析的結(jié)果表明,與經(jīng)典的基于高斯分布的非下采樣小波(NSW)消噪和NWP消噪相比,本文方法可以更有效地去除噪聲,同時(shí)更好地保留信號(hào)中的微脈沖等細(xì)節(jié)信息,在信噪比(SNR)、均方根誤差(RMSE)、相關(guān)系數(shù)(CC)和峰值相對(duì)誤差(REPV)等都有較好的改善。
[Abstract]:In order to improve the de-noising effect of pulsar signal, a local Laplace model de-noising method based on non-downsampling wavelet packet decomposition is proposed. Firstly, the real pulsar signal is decomposed by NWP, and the distribution characteristics of the real pulsar signal NWP coefficient are analyzed, and the Laplace distribution model of the real pulsar signal wavelet packet coefficient is established, and then the Laplace priori probability distribution is based on the Laplace priori probability distribution. According to the maximum posterior probability (MAPP) estimation criterion, the wavelet packet coefficient of the noisy pulsar signal is used to effectively estimate the wavelet packet coefficient of the real pulsar signal, and the estimated wavelet packet coefficient is reconstructed by NWP. The de-noised pulsar signal is obtained. The experimental results of different pulsar signals show that the proposed method is more effective than the classical non-down-sampling wavelet Gao Si based de-noising and de-noising methods. At the same time, the detailed information such as micropulse in the signal is better preserved, and the SNR, RMSE, CCV and relative peak error are improved in SNR, RMSE, RMSE, RMSE, RMSE, RMS, RMSE, RMS, RMSE, RMS, RMSE, RMS and RMSE, respectively.
【作者單位】： 武漢科技大學(xué)信息與計(jì)算科學(xué)系;武漢理工大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：國家自然科學(xué)基金(61473213,61671338)資助項(xiàng)目
【分類號(hào)】：TN911.7

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