稀疏贝叶斯学习理论在水声通信多普勒估计中的应用研究
发布时间：2018-10-05 08:08
【摘要】：在高速水声通信中,当信号经过水声稀疏信道后,通常会因为多径效应、时间延迟衰落、频率选择性衰落等问题导致信号发生畸变。同时在移动通信系统中,接收端和发送端有一定的相对移动速度,这将导致多普勒效应的产生。多普勒效应在信号上表现为信号的扩展,使信号在传输过程中产生畸变,导致在对接收信号进行处理时并不能得到正确的信息。所以在移动水声通信中,正确的实现多普勒因子的估计以方便后续的补偿处理是十分必要的。由于水声信道的内在稀疏性,近年来研究大热的压缩感知原理也可以用在对水声信号的处理上,所以本文中我们将水声多普勒估计问题转化为对水声信号的处理问题,利用该理论指导研究。重构算法上我们选择稀疏贝叶斯学习理论,其不需要参数控制而只需要算法内部自行迭代删除无用的训练样本与核函数这一特性完美契合了水声信道中稀疏度无法确定的特点。所以本文以基于稀疏贝叶斯学习理论的水声信号处理为研究方向对水声多普勒估计进行研究。本文先简要介绍了水声OFDM等相关技术,并根据水声信道具有内在稀疏性的特点提出了新的水声OFDM多普勒估计算法。该方法利用SBL算法对梳状导频经过水声信道后的接收信号进行重构,然后算得信道冲击相应,然后结合自相关函数原理利用信道冲击相应求得水声OFDM多普勒频移。仿真分析显示在与传统多普勒估计方法的对比中,该方法具有更高的估计精度及研究深度。随后,我们分析了现在水声通信网络中主要研究的MIMO-OFDM系统,并针对该系统提出了新的多普勒估计方案。在该系统中需要考虑多发收水声信道变化的稀疏性对多普勒因子估计的影响,即发送端信号经过水声稀疏信道后,只有少部分信号通过水声信道到达接收端被水听器接收处理,并且可能出现不同的频偏与延迟。针对这种形式,通过设计出联合导频的同步码并且充分利用同步码信号的特点,利用稀疏贝叶斯理论对系统传输过程中出现的不同多普勒因子进行精确估计。仿真表明,利用该算法进行MIMO-OFDM多普勒估计不仅能够估计出信号经过不同子信道产生的多普勒,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.
【学位授予单位】：江苏科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN929.3
，
本文编号：2252634
[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.
【学位授予单位】：江苏科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN929.3
，
本文编号：2252634
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