【摘要】：矢量水聽(tīng)器與傳統(tǒng)的聲壓水聽(tīng)器相比具有很多的優(yōu)勢(shì),而由其組成的矢量陣更能提高陣列的性能。MUSIC算法是陣列信號(hào)處理中具有里程碑意義的一類(lèi)算法,但它的缺陷在于性能?chē)?yán)重依賴于信噪比,其分辨性能會(huì)隨著信噪比的降低而顯著下降。本文提出了一種基于EMD去噪與MUSIC算法相結(jié)合的聯(lián)合估計(jì)方法。其基本思路是：首先用本文構(gòu)造的基于噪聲統(tǒng)計(jì)特性的EMD去噪方法對(duì)各基元接收到的信號(hào)分別進(jìn)行EMD去噪預(yù)處理,再將去噪后的數(shù)據(jù)進(jìn)行MUSIC方位估計(jì)。本文前半部分研究了EMD算法以及其在信號(hào)去噪中的應(yīng)用。文中深入探討了噪聲及噪聲經(jīng)EMD分解后各個(gè)分量的統(tǒng)計(jì)特性。在假定首個(gè)模態(tài)分量為噪聲的前提下,提出了一種基于噪聲統(tǒng)計(jì)特性的EMD去噪新方法。首先,將經(jīng)過(guò)EMD分解得到的首個(gè)模態(tài)分量每次隨機(jī)排序并與其他的分量進(jìn)行重構(gòu),將多次重構(gòu)結(jié)果累加求平均后,由于噪聲的隨機(jī)特性,便得到了信噪比改善的信號(hào)分量：而后再對(duì)新分量重新進(jìn)行分解,重復(fù)該操作數(shù)次；最后,得到噪聲功率得到很大抑制的信號(hào)分量。該方法可以簡(jiǎn)化為：隨機(jī)排序-重構(gòu)-累加-求平均-再分解-重復(fù)前述操作的過(guò)程。仿真實(shí)驗(yàn)表明,該方法在低信噪比的信號(hào)去噪中表現(xiàn)了良好的性能,其為低信噪比去噪提供了新思路。本文后半部分深入研究了MUSIC算法的基本理論,并分析了影響MUSIC算法定向精度的各個(gè)因素。針對(duì)MUSIC算法在低信噪比時(shí)無(wú)法準(zhǔn)確對(duì)信號(hào)源進(jìn)行定向及對(duì)多目標(biāo)的分辨能力差的問(wèn)題,結(jié)合前半部改進(jìn)后的基于噪聲統(tǒng)計(jì)特性的EMD去噪新方法,通過(guò)仿真實(shí)驗(yàn)表明,MUSIC空間譜的主波束寬度銳化,旁瓣降低,可提高對(duì)信號(hào)源的定向精度及對(duì)多目標(biāo)的分辨能力。
[Abstract]:The vector hydrophone has many advantages compared with the traditional acoustic hydrophone, and the vector array composed of vector hydrophone can improve the performance of the array. MUSIC algorithm is a kind of landmark algorithm in array signal processing. But its performance depends heavily on signal-to-noise ratio (SNR), and its resolution performance will decrease with the decrease of SNR. In this paper, a joint estimation method based on EMD denoising and MUSIC algorithm is proposed. The basic ideas are as follows: firstly, the EMD denoising method based on noise statistics is used to pre-process the received signals by EMD, and then to estimate the MUSIC azimuth of the de-noised data. In the first half of this paper, the EMD algorithm and its application in signal denoising are studied. The statistical properties of each component of noise and noise decomposed by EMD are discussed in detail. Under the assumption that the first modal component is noise, a new EMD denoising method based on the statistical characteristics of noise is proposed. First of all, the first modal component obtained by EMD decomposition is sorted randomly each time and reconstructed with other components. After the multiple reconstruction results are accumulated to average, because of the random characteristic of noise, The signal component with improved signal-to-noise ratio (SNR) is obtained: then the new component is decomposed again and the operation is repeated several times; Finally, the noise power is greatly suppressed. The method can be simplified as: random sort-refactoring-accumulative-average-refactoring-repeating the previous operation. The simulation results show that the proposed method performs well in signal denoising with low signal-to-noise ratio (SNR) and provides a new idea for de-noising with low signal-to-noise ratio (SNR). In the second half of this paper, the basic theory of MUSIC algorithm is deeply studied, and the factors that affect the orientation accuracy of MUSIC algorithm are analyzed. Aiming at the problem that the MUSIC algorithm can not accurately orient the signal source at low signal-to-noise ratio (SNR) and has poor resolution to multiple targets, combined with the improved EMD denoising method based on the noise statistical characteristics in the first half, the simulation results show that the proposed method can be used to solve the problem. The main beam width of MUSIC spectrum is sharpened and the sidelobe is reduced, which can improve the orientation accuracy of signal source and the resolution of multi-target.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TN911.4

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本文編號(hào)：2404142