Research on Time Reversal Spread Spectrum Underwater Acoustic Communication in Time-varying Channels

随着海洋开发、海洋环境监测和海洋国防安全等领域信息化建设的快速发展，利用海洋信道进行信息传输的需求大大增加，水声通信越来越受到人们的重视。但是，水声环境的复杂性、时变性和不可确定性导致信号多普勒频移、多径效应、幅度和频率选择性衰落等问题，给高性能水声通信带来了极大的挑战。 时间反转技术近年来成为水下通信的热点研究技术，其最大的优点是可在没有任何环境先验知识的情况下实现多径聚焦，提高检测信噪比。直接序列扩频技术具有抗干扰、抗多径衰落、可提高扩频增益等优点已经在水声通信中广泛应用。 考虑到时变信道下时间反转性能的下降，结合时间反转和扩频通信的优点，本文提出可适应时变信道的时间反转扩频通信方案，...With the rapid development of marine exploitation, marine environment monitoring, marine national defense and security, the requirement of transmitting information through underwater channel is increasing in recent years, so underwater acoustic communication has drawn significant attention. However, the complexity, time-varying and uncertainty of underwater acoustic channel character lead to Doppl...学位：理学硕士院系专业：海洋与环境学院_海洋物理学号：3112010115129

Compressed Sensing Estimation of Underwater Acoustic MIMO Channels Based on Temporal Joint Sparse Recovery

多输入多输出(MIMO)水声通信技术可以在极其有限的水声信道频带资源内提高信道容量,但多径和同道干扰的同时存在,使传统信道估计算法如最小二乘算法、压缩感知估计算法的性能急剧下降。考虑到通信数据块间水声信道多径结构存在一定的相关性,该文利用这种数据块间多径结构的时间域相关性建立水声MIMO信道的时域联合稀疏模型,并利用同步正交匹配追踪算法进行多个数据块联合稀疏恢复信道估计,提高MIMO信道多径稀疏位置的检测增益并抑制同道干扰,提高水声MIMO信道的估计性能。仿真和MIMO水声通信海试实验表明了所提方法的有效性。Multiple-Input-Multiple-Output(MIMO) under water acoustic communication is capable of improving the channel capacity in extremely limited bandwidth. However, the performance of traditional channel estimation algorithms, such as Least Squares(LS) method, Compressed Sensing(CS) method decreases rapidly because of the simultaneous presence of the Co-channel Interference(Co I) and multipath. As the sparse multipath structures between adjacent data blocks exhibit temporal correlation features, in this paper, the temporal correlation of sparse multipath structures is exploited to establish temporal joint sparse MIMO channel estimation model, and the Simultaneous Orthogonal Matching Pursuit(SOMP) algorithm is utilized for compressed sensing estimation of MIMO channels. Simulation and sea trial results validate the effectiveness of the proposed method.国家自然科学基金(11274259;11574258);; 福建省自然科学基金(2015J01172)~

Jointing sparse recovery estimation algorithm of underwater acoustic channels with long time delay spread

对具有长时延扩展的水声信道,传统的信道估计算法如最小二乘法将在大量零值抽头产生严重的估计噪声,导致估计性能下降,同时信道估计时所需的较高估计器阶数大大提高了运算复杂度。压缩感知信道估计方法可有效利用多径稀疏特性改善性能,但需采用较大的训练序列长度以保证稀疏恢复精度,由此导致额外的系统开销。利用水声信道多径稀疏结构在数据块间存在的相关性,建立基于分布式压缩感知的长时延水声信道联合稀疏模型,从而可利用同步正交匹配追踪算法进行联合重构,以进一步减小系统的训练序列开销,提高估计性能。最后通过仿真和海上实验验证了所提方法的有效性。Efficient estimation of underwater acoustic channels with a large time delay spread was addressed. For the conventional channel estimation methods such as LS, this type of channel estimation would produce serious estimation noise in zero-value taps which lead to poor performance of channel estimation. At the same time, a large time delay spread posed significant difficulties such as large channel order and the corresponding huge computation complexity. Compressed sensing(CS) channel estimation algorithm offered a solution to this problem by exploiting the sparsity of channel to improve the estimation performance. However, to ensure acceptable estimation performance, a long training sequence was needed, which unfortunately would cause additional overhead. A method was proposed which exploiting the joint correlation of sparse multipath structure between adjacent data blocks to deal with the estimation of long time delay channels under the framework of distributed compressed sensing(DCS).Thus the large time delay underwater acoustic channels can be jointly reconstructed by the simultaneous orthogonal matching pursuit(SOMP) algorithm to facilitate the system overhead reduction and estimation performance improvement. Simulation as well as the sea trial results indicate the effectiveness of the proposed method.国家自然科学基金资助项目(No.11274259,No.11574258)~

Estimation algorithm for sparse channels with gradient guided p-norm like constraints

为克服l0和l1范数约束的最小均方算法在不同信道稀疏程度下对稀疏信道估计中出现的收敛性能起伏较大等缺点,提出一种新的似P范数约束的最小均方算法,通过在最小均方算法代价函数中引入P值可变的似P范数约束以适应信道的不同稀疏程度,并在验证代价函数凸性的基础上导出P值的梯度导引寻优。最后给出仿真实验及其讨论,实验结果表明了新算法的优越性。The l0 and l1 norm constrained least mean square(LMS) algorithm can effectively improve the performance of the sparse channel estimation, but the convergence performance of such algorithms will considerably vary when the channel exhibits different sparisity.A novel p-norm like constraint LMS algorithm to accommodate the various sparisity of the channels through the introducing of the variable p-value was presented.Furthermore, the gradient guided optimization of the p-value was derived.Numerical simulation results are given to demonstrate the superiority of the new algorithm.国家自然科学基金资助项目(11274259); 教育部高等学校博士点专项基金资助项目(20120121110030)~

A Fast Sparse Signal Recovery Algorithm Based on Approximate l_0 Norm and Hybrid Optimization

欠定系统(又称超完备系统)的稀疏信号恢复在压缩感知、源信号分离和信号采集等领域中被广泛研究.目前这类问题主要采用l1范数约束结合线性规划优化或贪婪算法进行求解,但这些方法存在收敛速度慢、恢复精度不高等缺陷.提出一种快速恢复稀疏信号的算法,该算法采用一种新的近似l0范数代替l1范数构造代价函数,并融合牛顿法和最陡梯度法推导出寻优迭代式,以获得似零范数代价函数的最优解.仿真实验和真实数据实验结果表明,与经典算法相比,该算法在能提供相同精度、甚至更好精度的条件下,收敛速度更快.Obtaining sparse solutions of under-determined, or over-complete, linear systems of equations has found extensive applications in signal processing of compressive sensing, source separation and signal acquisition.However, the previous approaches to this problem, which generally minimize the l1 norm using linear programming(LP) techniques or greedy methods, are subject to drawbacks such as low accuracy and slow convergence.This paper proposes to replace the l1 norm with a newly defined approximate l0norm(AL0), the optimization of which leads to the derivation of a hybrid approach by incorporating the steepest descent method with the Newton iteration.Numerical simulations and real data experiment show that the proposed algorithm is about two to three orders of magnitude faster than the state-of-the-art interior-point LP solvers, while providing the same(or better) accuracy.国家自然科学基金(11274259); 教育部高等学校博士点专项基金(20120121110030)资助~

Two-parameter Adjustable Underwater Acoustic Channel Equalization Algorithm

针对浅海水声信道固有的随机时-空-频变、高噪、强多径等特性及变化的多径时延扩展,在变步长最小均方(lMS)平行滤波器组(Pfb-lMS)算法的基础上提出了一种新的水声通信自适应均衡算法。该算法将变阶数和变步长的调整结合起来,降低了算法对迭代步长和均衡器阶数的敏感度。仿真结果表明,新算法在参数适应性方面优于传统lMS及Pfb-lMS算法。The shallow water acoustic channel is characterized as a complex time,space and frequency-variant channel with several negative factors,e.g.narrow band,high ambient noise,multipath distortion and polytropic multipath time delay which pose serious difficulty for the underwater acoustic communication.A novel two-parameter adjustable least mean square(LMS) equalization algorithm is presented based on the classic parallel filter banks LMS(PFB-LMS) algorithm.The new algorithm enables hybrid adjustment of step-size and tap-length so that the sensitivity of step-size and tap-length parameter selections is alleviated.Simulation results show that the new algorithm outperforms the traditional LMS and PFB-LMS algorithms in parameter robustness under time varying channels.国家自然科学基金项目(11274259); 教育部高等学校博士学科点专项基金项目(20120121110030

Research on Time Reversal Spread Spectrum Underwater Acoustic Communication under Low SNR

直接序列扩频提供了一种低信噪比条件下水声通信的有效手段,但在低信噪比条件下直扩系统常用的信道均衡,rAkE接收机等抑制多径干扰方法性能下降。该文结合具有突出时间、空间聚焦能力的多通道被动时间反转技术与直接序列扩频抑制低信噪比条件下多径干扰的影响,并采用卷积纠错编码进一步提高通信性能。湖试结果证明了该技术方案的有效性。Direct Sequence Spread Spectrum(DSSS) provides an effective way for underwater acoustic communication under low Signal-to-Noise Ratio(SNR).However,low SNR will deteriorate the common multi-path mitigation approaches employed in DSSS system such as channel equalizer and rake receiver.Considering that multi-channel time reversal has the ability to refocus the energy in spatial and temporal domain,multi-channel time reversal is incorporated with DSSS technology to suppress the multipath interference under low SNR,also convolutional coding is adopted to further improve the communication performance.Lake trial results show the effectiveness of the proposed scheme.国家自然科学基金(10704063);福建省自然科学基金(2011J01275)资助课

Underwater acoustic speech communication using time reversal and time-frequency differential ofDM methods

针对水声信道多径、时变、多普勒等恶劣传输特点对水声语音通信的严重影响,本文采用多通道时间反转和时频差分OfdM进行水声语音通信技术方案设计,该方法首先通过多通道时间反转进行时间域和空间域多径聚焦,进而结合时频差分OfdM调制解调抑制残留多径的影响。由于无需采用信道估计和均衡算法,系统实现方便、复杂度低,同时对信道具有一定程度的稳健性。该方法语音压缩编码采用混合激励线性预测编码。仿真实验和海试实验表明了本文方案的有效性。The difficulties of underwater acoustic channel, i.e., multipath, time varying and Doppler shifting pose significant challenges to underwater acoustic speech communication.In this paper, multi-channel time reversal is incorporated with time-frequency differential orthogonal frequency division multiplexing(ofDM)technology to design an underwater acoustic speech communication system, which enables time-frequency domain focusing of multipath by multi-channel time reversal, as well as suppressing of the residual multipath with time-frequency differential ofDM.Thus the employment of complicated channel estimation and equalization are avoided to facilitate the low complexity system implementation.In addition, the mixed-excitation linear predictive(MELP) is employed for speech encoding.The simulation and sea trial demonstrate the effectiveness of the method at presence of time varying multipath underwater acoustic channel.国家自然科学基金资助项目(11274259

多频带水声信道的时频联合稀疏估计

多频带水声信道多径结构在相邻数据块和不同子频带存在相关性,从分布式压缩感知的角度可对这种时频联合稀疏特性进行利用。但是,在传统联合稀疏模型下水声信道间存在的不同多径时延部分形成差异支撑集,由此引入的干扰导致估计性能下降,提出利用多路径选择机制进行差异支撑集检测;同时,进一步结合频域子频带信道间、时域相邻数据块信道间存在的相关性进行频带-时间域联合稀疏估计.利用数值仿真及海试实验结果进行了性能验证和比较,表明利用时频联合稀疏估计构造的水声通信接收机改善了匹配性能,可获得较为明显的输出信噪比、误比特率等通信性能提升.从而说明:利用多频带水声信道在时域、频域存在的联合相关性可有效提高信道估计性能。国家自然科学基金项目(11574258,11274259);;\n声呐技术重点实验室基金项目“基于信道感知的水声通信网络技术研究”资

Non-uniform Norm Constraint Estimation Algorithm for Underwater Acoustic Channels at the Presence of Varying Sparsity

对于具有典型时频双重扩展特性的水声信道,利用其稀疏分布特性在估计算法中引入范数约束可提高信道估计性能。但当水声信道多径稀疏度变化时,经典的l0或l1范数约束由于缺乏对不同稀疏模式的适应性,将导致性能下降。通过引入非均匀范数约束自适应算法并对其进行收敛性分析,针对水声信道稀疏度变化利用该算法通过非均匀范数的形式提高适应性。不同接收深度水声信道的仿真及海上实验结果表明,该算法相对经典的l0或l1范数约束算法有较明显的性能改善。For the typical underwater acoustic channels with time-frequency double extension characteristics,the channel estimation performance can be improved by introducing a norm constraint into the channel estimation algorithm based on the sparse distribution feature of the channels.However,at the presence of varying multipath structure caused by change of depth or velocity gradient,the classic l0 or l1norm constraint methods are subject to performance degradation due to lack of adaptability to sparsity.A previously derived non-uniform norm constraint LMS( NNCLMS) algorithm is introduced,and then a convergence analysis is made on it.In the form of non-uniform norm,the NNCLMS algorithm is used to accommodate the different sparsities caused by different multipath structures.Numerical simulation and sea experimental results show that the estimation performance of the proposed method is superior to that of the classic l0 or l1norm constraint algorithm.国家自然科学基金项目(11274259); 东南大学水声信号处理教育部重点实验室开放研究基金项目(UASP1305