部分范数约束的稀疏恢复算法及其在单载波水声数据遥测中的应用

对于单载波水声数据压缩与恢复问题,压缩感知能以较低能耗获得信号压缩与恢复效果。但压缩感知核心目标是直接求最小l0范数,该问题表现为NP难问题,因此,常将其转化为求l1范数约束最小化问题,而求l1范数约束最小化的稀疏解精度有限。基于此,推导出基于部分范数约束的稀疏信号恢复算法,该算法通过部分范数约束在拉格朗日求解中增加一个零吸引项,从而动态分配稀疏抽头的软阈值。同时,该算法用于实际海上数据的遥测,结合离散余弦变换（DCT）,可将单载波水声数据恢复精度提高。国家自然科学基金资助项目(No.61701405);;中央高校基本科研业务费专项资金资助项目(No.3102017OQD007);;中国博士后科学基金资助项目(No.2017M613208)~

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~

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

Transmission shielding technology for bistatic sonar

基线附近区域为双基地声呐的探测盲区,当目标位于基线区域时,目标回波与强直达波干扰同时同向到达,基于空域滤波等的传统直达波抑制方法失效。为实现双基地声呐对盲区内目标的探测,提出了一种适用于多发射阵元系统的发射声屏蔽技术。发射声屏蔽技术利用目标回波与直达波相异的多途信道结构特性,自动屏蔽在接收站位置处的直达波而不影响目标回波,且不需接收站进行任何后续处理。在已知回波信道信息时,发射声屏蔽技术可进一步实现对回波信号的聚焦,提高信干比。仿真结果表明,在不同信道条件下发射声屏蔽均能有效抑制直达波干扰。利用发射声屏蔽技术,双基地声呐能够实现对基线区内目标的检测。The area near the baseline is a dead zone for bistatic sonar.The echo and the strong direct wave will arrive in the same place simultaneously when the target is in the baseline area and the direct wave suppression method based on a spatial filter will fail.To detect targets in the dead zone,a transmission shielding method for a multi-element transmit system is proposed.The transmission shielding automatically suppresses the direct wave at the receiving station utilizing structural differences in multiple channels between the echo and the direct wave.The transmission shielding further focuses on the echo,so as to improve the Signal-Noise Ratio,using known echo channel information.The simulation results show the transmission shielding method can suppress direct wave interference for different multipath channels.Bistatic sonar can detect targets in the baseline area utilizing the transmission shielding method.国家自然科学基金资助项目(51179034); 海洋工程国家重点实验室(上海交通大学)资助项目(1211

Approximation Norm Estimations of Sparse Underwater Acoustic Channel

声波是目前水下无线通信方式中广泛采用的信号传输媒介，且水下声传播面临着与陆地无线电通信中不曾有的困难和挑战，如:随载波频率增加而衰减严重;传播路径的多变性;以及速度约为1500m/s的传播速度。这些约束使得水声信道具有较为明显的时延多普勒双扩展等现象。 论文以水声通信系统为应用背景，针对稀疏水声信道下的可靠通信问题，以水声信道稀疏特性-信道建模-信道估计-均衡器输出结果验证为主线，着重研究水声信道估计算法。在算法理论研究的基础上，结合多次数值仿真和海试实验;进而通过数据处理分析，验证论文方案的有效性。 本文结合稀疏水声信道估计的实际应用场景以及压缩感知领域中出现的稀疏信号恢复方法，...Sound is a commonly adopted medium in underwater wireless communication, in addition, underwater acoustic communication faces the difficulties and challenges which haven't been encountered in the terrestrial wireless communication, e.g. signal strength falls off with carrier frequency; the variability of the propagation paths; and the transmitted speed is limited to round of 1500 m/s. All these co...学位：理学博士院系专业：海洋与地球学院_海洋物理学号：3112011015361

Improved compressed sensing estimation of block sparse underwater acoustic channel

压缩感知信道估计可利用信道稀疏特性提高估计性能,但对于具有典型块稀疏分布的水声信道,经典的l_0或l_1范数无法很好地描述块稀疏特性。利用水声信; 道块稀疏分布规律特性提出一种能够识别块稀疏结构的块稀疏似零范数,并在稀疏恢复信道估计算法中引入块稀疏似零范数约束项,进一步推导了复数域块稀疏似零; 范数恢复迭代算法,该算法通过对块稀疏似零范数进行梯度下降迭代并将梯度解投影至解空间来获得水声信道的块稀疏似零范数估计.数值仿真和海上水声通信实验; 结果表明该算法相对经典的稀疏信道估计算法有较明显的性能改善。通过算法推导、仿真和实验可获取结论:利用水声信道的块稀疏特性进行压缩感知重构可有效提; 高信道估计性能.For sparse underwater acoustic channels, compressed sensing methods can; be adopted to improve the estimation performance. The classic l_0 or l_1; norm, however, are limited in describing the block sparse distributed; characteristics of the underwater acoustic channel. We introduce the; block sparsity identification term, i.e. block sparse approximated l_0; norm (BAL0) to address this problem. By adopting complex projected; gradient method and then projecting the gradient solution to a set of; the underwater acoustic channel solution space, an iterative algorithm; is derived to solve the complex-field BAL0 norm channel estimation. Both; the numerical simulation and experimental results show that the proposed; algorithm has significant performance improvement compared with classic; sparse signal recovery algorithms. By the derivation of the algorithm,; simulations and at-sea experiment, one can conclude that the estimation; quality of underwater acoustic channel can be improved by exploiting its; block sparsity in compressed sensing reconstructions.国家自然科学基金项目; 教育部高等学校博士点专项基

Distributed compressed sensing estimation of underwater acoustic multiple-input-multiple-output channels

多输入多输出技术通过采用多个阵元进行多发多收空间复用信道可在极其有限的通信带宽下实现高速水声通信,但由于同时存在通道间干扰和多径干扰,水声MIMO信道估计变得困难。提出利用MIMO水声信道多径稀疏结构存在的相关性,在经典联合稀疏模型的基础上对MIMO观测矩阵进行重组,从而建立基于分布式压缩感知的单载波水声MIMO通信信道联合稀疏模型;同时,针对信道响应中具有相同多径位置的稀疏部分和特有稀疏部分设计区分性正交匹配追踪算法进行联合重构,进一步抑制通道间干扰的影响。最后通过仿真和海上实验进行本方法有效性的验证,实现16 kbPS的MIMO水声通信。通过算法推导、仿真和实验可得到结论:利用MIMO水声信道多径相关性进行分布式压缩感知估计可提高估计性能。MIMO(Multiple Input Multiple Output) technology provides a potential solution for high data rate underwater acoustic communications under limited bandwidth.However,simultaneous presence of multipath and co-channel interference(Co-channel interference,CoI) poses significant difficulty to estimation of acoustic MIMO channels with the conventional method such as classic algorithms or compressed sensing(CS) algorithms.To exploit the spatial correlation feature of the acoustic MIMO channels,the distributed compressed sensing(DCS) acoustic MIMO channel estimation model based on re-organizing of the MIMO measurement matrix is proposed.By discriminatively estimating the sparse components with the same time delay and those with different time delay,a novel DOMP(Discriminative Orthogonal Matching Pursuit) algorithm is designed to facilitate enhanced estimation of multipath components,as well as alleviation of the CoI.Numerical simulations as well as sea trial experiments are provided to demonstrate the superior performance of the proposed method.国家自然科学基金(11274259); 教育部高等学校博士学科点专项基金(20120121110030)资

Dual-parameter adjustable least mean square algorithm for underwater acoustic channel equalization

作为一种降低因水声多途引起的码间干扰的有效手段,水声信道均衡技术正引起广泛关注.现有的算法中,最小均方算法及其变型因其计算量低而被广为应用.而采用平行滤波器组的变步长法可提高该算法在时变环境中的性能,却未出现该类算法在水声信道动态阶数下的性能研究.本文提出将滤波器步长和长度双参数进行调节的平行滤波器组用于时变水声信道均衡.双参数调整机制能有效增强算法对时变水声信道的容忍度.仿真和真实数据的实验验证了新算法的优越性.As a potentially effective method to mitigate inter symbol interference caused by multi-path,channel equalization of underwater acoustic communication has attracted considerable attention.Among existing algorithms that can be found in the literature,the classic least mean square(LMS)and various variants of it are of particular interest for practical implementation due to their low computational complexity.However,as the variable step size as well as the parallel filter bank structure can improve the performance of LMS type algorithms under time varying environment,there is a lack of investigation on their adaptability to the dynamic order of underwater acoustic channels.In this paper,a new dual-parameter,adjustable method is presented which embeds the variable step size and filter length into the parallel filter bank LMS algorithm for equalization of time varying underwater acoustic channel.The mechanism of dual parameter(step size and filter length)adjustment ensures that the proposed algorithm has better tolerance upon the time variations caused by either specific coefficients or the order of the channel response.Both numerical simulations and real data experiments show that the performance of the new method outperforms the classic methods.NationalNaturalScienceFoundationofChina(11274259); NaturalScienceFoundationofFujianProvince;China(2011J01275); ScienceandTechnologyProjectofXiamenCity(3502z20113008

Dynamics of soil organic carbon in Caragana microphylla forest and its relationship with environment factors in loess hilly region

以黄土丘陵区柠条人工林为研究对象,采用野外调查与室内分析相结合的方法,探讨柠条生长过程中土壤有机碳储量的变化规律。结果表明:1)土壤有机碳主要分布在0～20 cm土层,占0～50 cm土层总储量的49%～63%;2)相对于对照地,柠条林地土壤有机碳储量随柠条生长年限的增加先减小再升高最后趋于稳定,10、26、40、50 a柠条林地土壤总有机碳储量分别为1.555、3.236、2.775、2.444 kg/m2,26 a林地土壤有机碳储量最高,随林龄增大其变化趋于稳定;3)相关性分析结果表明,土壤有机碳质量分数与土壤密度之间呈显著负相关关系,各林地土壤密度随柠条生长年限的增加而减小,说明柠条可以通过改变土壤性质间接增加土壤总有机碳储量,土壤有机碳质量分数与根系生物量、土壤全氮质量分数之间呈极显著正相关关系,说明柠条的根系生长和固氮特性有助于有机碳的积累