【摘要】：在高速水聲通信中,當信號經(jīng)過水聲稀疏信道后,通常會因為多徑效應(yīng)、時間延遲衰落、頻率選擇性衰落等問題導(dǎo)致信號發(fā)生畸變。同時在移動通信系統(tǒng)中,接收端和發(fā)送端有一定的相對移動速度,這將導(dǎo)致多普勒效應(yīng)的產(chǎn)生。多普勒效應(yīng)在信號上表現(xiàn)為信號的擴展,使信號在傳輸過程中產(chǎn)生畸變,導(dǎo)致在對接收信號進行處理時并不能得到正確的信息。所以在移動水聲通信中,正確的實現(xiàn)多普勒因子的估計以方便后續(xù)的補償處理是十分必要的。由于水聲信道的內(nèi)在稀疏性,近年來研究大熱的壓縮感知原理也可以用在對水聲信號的處理上,所以本文中我們將水聲多普勒估計問題轉(zhuǎn)化為對水聲信號的處理問題,利用該理論指導(dǎo)研究。重構(gòu)算法上我們選擇稀疏貝葉斯學(xué)習(xí)理論,其不需要參數(shù)控制而只需要算法內(nèi)部自行迭代刪除無用的訓(xùn)練樣本與核函數(shù)這一特性完美契合了水聲信道中稀疏度無法確定的特點。所以本文以基于稀疏貝葉斯學(xué)習(xí)理論的水聲信號處理為研究方向?qū)λ暥嗥绽展烙嬤M行研究。本文先簡要介紹了水聲OFDM等相關(guān)技術(shù),并根據(jù)水聲信道具有內(nèi)在稀疏性的特點提出了新的水聲OFDM多普勒估計算法。該方法利用SBL算法對梳狀導(dǎo)頻經(jīng)過水聲信道后的接收信號進行重構(gòu),然后算得信道沖擊相應(yīng),然后結(jié)合自相關(guān)函數(shù)原理利用信道沖擊相應(yīng)求得水聲OFDM多普勒頻移。仿真分析顯示在與傳統(tǒng)多普勒估計方法的對比中,該方法具有更高的估計精度及研究深度。隨后,我們分析了現(xiàn)在水聲通信網(wǎng)絡(luò)中主要研究的MIMO-OFDM系統(tǒng),并針對該系統(tǒng)提出了新的多普勒估計方案。在該系統(tǒng)中需要考慮多發(fā)收水聲信道變化的稀疏性對多普勒因子估計的影響,即發(fā)送端信號經(jīng)過水聲稀疏信道后,只有少部分信號通過水聲信道到達接收端被水聽器接收處理,并且可能出現(xiàn)不同的頻偏與延遲。針對這種形式,通過設(shè)計出聯(lián)合導(dǎo)頻的同步碼并且充分利用同步碼信號的特點,利用稀疏貝葉斯理論對系統(tǒng)傳輸過程中出現(xiàn)的不同多普勒因子進行精確估計。仿真表明,利用該算法進行MIMO-OFDM多普勒估計不僅能夠估計出信號經(jīng)過不同子信道產(chǎn)生的多普勒,且其具有一定的抗噪能力。
[Abstract]:In high speed underwater acoustic communication, when the signal passes through the channel of acoustic sparsity, the signal is usually distorted because of multipath effect, time delay fading, frequency selective fading and so on. At the same time, in the mobile communication system, the receiver and the transmitter have a certain relative moving speed, which will lead to the generation of Doppler effect. The Doppler effect in the signal is the spread of the signal, which makes the signal distortion in the process of transmission, resulting in the processing of the received signal can not get the correct information. Therefore, in mobile underwater acoustic communication, it is very necessary to realize the Doppler factor estimation to facilitate the subsequent compensation processing. Due to the inherent sparsity of underwater acoustic channel, in recent years, the compression sensing principle of great heat can also be used in the processing of underwater acoustic signals, so in this paper, the problem of underwater acoustic Doppler estimation is transformed into the processing of underwater acoustic signals. The theory is used to guide the research. In the reconstruction algorithm, we choose sparse Bayesian learning theory, which does not need parameter control, but only needs the algorithm to iterate and remove useless training samples and kernel function, which fits perfectly with the characteristics of indeterminate sparsity in underwater acoustic channel. Therefore, the underwater acoustic Doppler estimation based on sparse Bayesian learning theory is studied in this paper. In this paper, the underwater acoustic OFDM and other related techniques are briefly introduced, and a new underwater acoustic OFDM Doppler estimation algorithm is proposed according to the inherent sparsity of underwater acoustic channel. In this method, SBL algorithm is used to reconstruct the received signal of comb pilot after passing through the underwater acoustic channel, and then the channel impulse response is calculated, and then the Doppler frequency shift of underwater acoustic OFDM is obtained by using the principle of autocorrelation function. The simulation results show that the proposed method has higher estimation accuracy and research depth than the traditional Doppler estimation method. Then we analyze the MIMO-OFDM system which is mainly studied in the underwater acoustic communication network and propose a new Doppler estimation scheme for the system. In this system, the influence of the sparsity of multi-receiving channel on Doppler factor estimation should be considered. That is, only a few signals are received by hydrophone when the signal passes through the channel of acoustic sparsity, and only a few of the signals reach the receiving end through the underwater acoustic channel, and only a few signals are received by hydrophone. And there may be different frequency offset and delay. In this form, by designing the joint pilot synchronization code and making full use of the characteristics of the synchronization code signal, the sparse Bayesian theory is used to accurately estimate the different Doppler factors in the transmission process of the system. Simulation results show that the MIMO-OFDM Doppler estimation algorithm can not only estimate the Doppler generated by different subchannels, but also has a certain anti-noise capability.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TN929.3
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本文編號：2252634