本文關(guān)鍵詞：擴(kuò)展自相關(guān)的正弦信號(hào)頻率估計(jì)算法　出處：《信號(hào)處理》2014年10期 　論文類型：期刊論文

  更多相關(guān)文章： 擴(kuò)展自相關(guān)函數(shù) 頻率估計(jì) 均方誤差 克拉美羅下界 正弦信號(hào)

【摘要】：本文針對(duì)混有高斯白噪聲的正弦信號(hào),提出了擴(kuò)展自相關(guān)的頻率估計(jì)算法。論文通過(guò)理論分析,充分挖掘自相關(guān)函數(shù)包含的頻率信息,推導(dǎo)出新的擴(kuò)展自相關(guān)函數(shù);同時(shí),在Yan算法的基礎(chǔ)上對(duì)頻率估計(jì)式加以改進(jìn)。仿真結(jié)果表明,與現(xiàn)有頻率估計(jì)算法相比,在信號(hào)序列較短或信噪比較低時(shí),本文算法的估計(jì)方差更接近克拉美羅下界(CRLB);與Yan算法相比,在序列較大或信噪比較高時(shí),在相同估計(jì)方差下,本文算法的計(jì)算量更小。
[Abstract]:In this paper, an extended autocorrelation frequency estimation algorithm is proposed for sinusoidal signals mixed with Gao Si white noise. Through theoretical analysis, the frequency information contained in the autocorrelation function is fully mined. A new extended autocorrelation function is derived. At the same time, the frequency estimation formula is improved on the basis of Yan algorithm. The simulation results show that compared with the existing frequency estimation algorithm, the signal sequence is shorter or the signal-to-noise ratio is lower. The estimated variance of this algorithm is closer to the lower bound of Clemero. Compared with Yan algorithm, when the sequence is larger or the SNR is higher, the computational complexity of this algorithm is less than that of Yan algorithm under the same estimated variance.
【作者單位】： 大連海事大學(xué)信息科學(xué)技術(shù)學(xué)院;大連理工大學(xué)城市學(xué)院;大連理工大學(xué)軟件學(xué)院;
【分類號(hào)】：TN911.23
【正文快照】： 1引言對(duì)淹沒(méi)于噪聲中的正弦信號(hào)進(jìn)行頻率估計(jì),無(wú)論在理論中,還是在實(shí)際應(yīng)用中,都具有非常重要的研究?jī)r(jià)值。國(guó)內(nèi)外眾多學(xué)者對(duì)此問(wèn)題做了大量的研究,提出了眾多頻域[1-4]、時(shí)域[5-13]分析方法。傳統(tǒng)基于時(shí)域自相關(guān)的方法,利用少量的自相關(guān)函數(shù)估計(jì)頻率,此類方法計(jì)算簡(jiǎn)單,但性能，

本文編號(hào)：1441520