水声信道线性均衡的研究

本文在对LMS和稀疏权自适应均衡算法进行分析的基础上，将其引入到水声数据传输中，利用LMS算法对时不变信道和慢变信道进行了仿真实验；利用稀疏权自适应均衡算法对深海信道进行了仿真实验，并获得了仿真结果。     

MPSK调制系统中的自适应信道估计

本文研究了在MPSK调制方式下,不发送训练序列的直接判决自适应信道估计问题。为提高信道估计性能,提出采用软判决鉴相估值来调整加权最小均方（WLMS）和加权递归最小二乘（WRLS）自适应算法中的权值来进行信道估计,并研究了改进的软判决WRLS算法。结果表明,软判决加权LMS算法比一般LMs性能有很大提高,改进的软判决WRLS算法估计性能优于加权RLS算法。     

有源噪声控制的一种变换域算法

有源噪声控制广泛应用于中低频噪声控制中。传统的有源控制都是在时域内使用LMS算法进行控制，由于LMS算法的收敛速度跟输入信号的自相关矩阵特征值分布有关，当特征值分布较大时，LMS算法的收敛速度会变的很慢。本文使用离散余弦变换(DCT)技术，在变换域内进行有源噪声控制的研究，同时考虑了变换域约束。仿真表明算法的有效性。     

变步长似p范数约束稀疏水声信道估计方法

针对范数约束类归一化最小均方(NLMS)算法在正交频分复用(OFDM)稀疏水声信道估计中误码率较高的问题,提出一种改进的变步长似p范数约束信道估计方法。采用改进双Logistic函数构造步长,并将误差信号自相关函数引入其中,实时调整步长和零吸引项,使得收敛速度和估计精度能够很好地折中。算法仿真结果表明,在浅海多径稀疏水声信道下,相比于传统方法,所提出的信道估计获得的最高性能提升为收敛速度提高72.3%,稳态误差降低95.9%。湖上试验数据处理结果显示,相比于传统信道估计方法,所提出的方法能使通信误码率降低2~3个数量级,实现零误码水声通信。      

时变水声信道的动态压缩感知估计

水声多径信道具有的稀疏特性已被应用于设计稀疏估计方法提高信道估计性能。然而,水声信道具有的快速时变特性,给传统稀疏信道估计方法带来了很大的困难。考虑到水声信道除了高时变性多径,还存在着相对静止,缓慢变化的直达径或者海底反射径,通过将水声信道建模为由静态和时变稀疏支撑集组成,把时变水声信道估计转化为动态压缩感知问题。结合卡尔曼滤波和压缩感知理论,并采用原始对偶追踪算法求解Dantzig selector模型,从而实现对复数域基于卡尔曼滤波器的压缩感知稀疏求解问题的处理。信道时变条件下的数值仿真及基于信道估计的判决反馈均衡器的海上实验结果表明,该算法相对经典的正交匹配追踪和最小二乘QR分解算法具有较明显的性能改善。从而说明,通过对时变水声信道进行动态压缩感知估计可有效提高估计性能。     

水声通信系统中双向turbo均衡算法

提出了一种水声通信系统中直接自适应双向turbo均衡算法。摒弃了信道估计步骤,采用基于直接自适应的turbo均衡器,并利用内嵌数字锁相环的判决反馈均衡器结构跟踪时变信道,采用最速优化算法自适应调整迭代步长,使得收敛速度和算法性能得到很好折中。此外,利用最小均方误差准则,得到最优权重因子,对正向与反向turbo均衡结果加权求和,消除误差传播效应。仿真和湖上实验验证了方法的正确性,双向均衡的性能优于单向均衡。湖上实验结果表明,基于直接自适应算法相比于基于信道估计的算法,对时变信道不敏感,能获得更低的误比特率。      

改进的信道稀疏度检测正交多载波多普勒估计方法

针对正交频分复用水声移动通信中频域变采样技术联合信道稀疏度检测的多普勒估计算法在冰水混合区复杂信道环境下多普勒估计效果欠佳且计算量大的问题,提出了一种改进的多普勒估计算法。采用时域重采样技术对信号进行不同压缩因子的补偿,避免了频域变采样技术中的高阶快速傅里叶变换运算,从而降低了算法复杂度。利用梳状导频位置向量结合时域变采样技术更准确提取的导频子载波,进而获得更加精确的信道估计和信道稀疏度检测结果,提高了多普勒估计精度。仿真结果和冰下试验结果表明,与原始算法相比,改进的方法在冰水混合水域复杂多径时变信道环境下可以有效实现对信号多普勒畸变的跟踪并且计算量显著下降,能够保障正交频分复用水声移动实时通信。      

基于混合范数约束的非均匀稀疏水声信道估计方法*

水声信道具有明显的簇状稀疏特性,即稀疏的信道冲激响应大部分为零或接近零的小值系数,而非零值系数是以簇的形式非均匀分布于时延域。为此本文提出了一种基于非均匀混合范数约束仿射投影算法的水声信道估计方法。该方法首先根据信道簇状结构对其进行非均匀分组,基于此将范数约束规则加入仿射投影算法中,具体方法为：对簇状部分施加范数约束,有效提高系数间的相关性,而簇状结构与其他零值抽头之间利用范数约束实现了整体的稀疏特性。数值仿真以及深海远程水声通信实验数据处理结果表明了本文所提出的水声信道估计算法相较现有稀疏信道估计方法能够实现更快的收敛速度以及更高精度的信道估计结果。     

基于动态阈值匹配追踪的主动声呐直达波抑制方法

双基地声呐中的直达波干扰可以通过重构抵消的方式进行抑制。这种方法需要对直达波信道进行估计，传统的正交匹配追踪算法的收敛速度慢，分步正交匹配追踪等算法在提高收敛速度时牺牲了信道估计精度，导致回波检测能力下降。本文提出一种动态阈值匹配追踪算法估计直达波的信道响应，在提高收敛速度的同时兼顾了信道估计精度。在仿真环境中，达到同样的直达波抑制效果，所提算法与传统正交匹配追踪算法相比，收敛速度显著提升，检测输出的回波强度比分步正交匹配追踪算法高4dB；海试数据处理结果中，所提方法迭代收敛速度较正交匹配追踪算法提升4倍；输出的回波强度比分步正交匹配追踪算法高2dB。     

起伏海面环境下水声信道特性及估计方法*

为了研究随机起伏海面的散射对水声信道特性的影响,以及不同信道估计算法在该信道条件下的性能问题,该文从利用Monte Carlo方法模拟的基于PM海浪谱的二维随机起伏海面出发,提取了与风向不同夹角的一维随机起伏海面,并利用Kirchhoff近似法计算了其散射强度。将得到的声散射特性和海面环境信息融入到信道模型中,建立了基于PM海浪谱的随机信道模型。通过仿真分析了不同风速、传播距离条件下,声波的传播损失和信道冲激响应变化,并利用l_0-最小均方误差法、匹配追踪算法和正交匹配追踪算法进行了信道估计。给出了不同环境条件下,三种算法估计性能的综合比较结果,验证了算法针对该信道模型的有效性。     

一种水声信道均衡器的混合算法

水声高速相干通信中使用自适应均衡对抗信道在接收信号中产生的码间串扰。目前,1/2分数间隔采样的判决反馈均衡器结构得到了广泛的应用。本文分析了几种自适应算法在此种均衡器结构下的性能,结合浅海水声信道的特点,提出了一种快速卡尔曼滤波和快速自优化最小均方的混合算法,模拟及海试结果表明,该混合算法在计算复杂度、收敛速度以及稳定性方面都有很好的性能,已经应用到了水声实时高速通信系统当中。     

水声通信系统中双向turbo均衡算法研究（英文）

A direct-adaptation based bidirectional turbo equalizer for underwater acoustic communications is proposed.Abandoning the channel estimation process,the direct-adaptation based turbo equalizer embedded with digital phase-locked loop is adopted to track time-varying channel.The fast self-optimized algorithm is used to adjust the step size,thus a good tradeoff between the convergence speed and performance has been made.Furthermore,by minimizing the mean squared error,an optimal weighting factor is derived to exploit bidirectional diversity gain.The forward turbo equalizer is combined with the backward turbo equalizer to eliminate error propagation effect.Simulated and experimental results demonstrate that the bidirectional turbo equalizer outperforms the single directional one.It can be seen from the experimental results that,compared with the channel estimation based algorithm,the direct-adaptation based algorithm is less sensitive to the time-varying channel and has a lower bit error rate.     

Joint fast time-varying channel estimation with noise elimination and ICI cancellation for LTE-V2X systems

Vehicle-to-everything (V2X) communication is essential for intelligent transportation systems (ITS) and critical technology to ensure traffic safety. Aiming at the problems of noise interference, time-varying, and inter-carrier interference (ICI) in the LTE-V2X channel, a joint fast time-varying channel estimation with noise elimination and ICI cancellation is proposed in this paper. Firstly, using the autocorrelation characteristics of the Zadoff Chu (ZC) pilot sequence, a modified discrete Fourier transform (M-DFT) channel estimation algorithm is proposed to eliminate the noise in cyclic prefix (CP). Secondly, a joint iterative direct decision (IDD) and time-varying channel fitting (CF), called IDD-CF channel estimation algorithm, is proposed to track the rapid changes of channels on different symbols and eliminate ICI. The system simulation results show that the proposed joint fast time-varying channel estimation algorithm can effectively eliminate the noise and ICI, improve the performance of channel estimation, and have better robustness under different Doppler frequency shifts than the representative channel estimation algorithm.     

Dynamic discrimination of convergence of the LMS time delay estimation in complicated noisy environments

For nonstationary signals, time constant can no longer represent the actual convergence performance of the adaptive delay estimation. A scheme is thus proposed for dynamically discriminating the convergence of the LMS time delay estimation to get an optimal estimate even under complicated noisy environments. Based on the fact that the filter output gets orthogonal to the estimation error only when an adaptive filter operates in its optimal condition, an approximate orthogonality constraint is derived for discriminating the practical adaptation’s convergence. This discrimination condition, which is obtained through the estimated mean-square values of related available variables at each iteration, can be updated in the adaptation process according to the varying statistic characteristics of observed signals. Thus, it can dynamically evaluate the quality of the adaptive solution and end the iteration opportunely. Simulation results are also included to demonstrate its effectiveness to give exact convergence results in both stationary and nonstationary cases.     

Adaptive RAKE receiver in chaos based pilot-added DS-CDMA system

This paper presents the theoretical analysis of adaptive multiuser RAKE receiver scheme in frequency selective fading channel for direct-sequence code division multiple access (DS-CDMA) system. Least mean square (LMS) algorithm is used to estimate the channel coefficients. Chaotic sequences are used as spreading sequence and corresponding bit error rate (BER) in closed form is derived for imperfect channel estimation conditions. Performances of chaotic sequences are compared with pseudorandom noise (PN) sequences. Under perfect synchronization assumption, various simulation results are shown to investigate the performance of the proposed system.     

一种采用脉冲串的自适应有源干扰抵消器

在许多应用中，周期性干扰是由旋转的机械设备所产生的。一个周期性信号可以与一个特殊形式的周期性的脉冲串完全相干。本文提出了一种采用脉冲串的自适应有源噪声抵消器(AANC)来抵消周期性干扰，这种抵消器产生一个与干扰信号相同步的周期性的单位脉冲串作为参考输入。将这种有源噪声抵消器和Delayed-X LMS算法相结合，可以大大地减小运算量，并且也可以保证良好的收敛特性。理论分析和计算机仿真验证了本文的结果。     

基于Dual-Mode MCMA+DD双模式盲均衡算法研究

为了兼顾误差性能和收敛速度,研究了将双模修正常量模算法和判决引导算法有机结合的双模式盲均衡算法.该算法在收敛初期采用双模修正常量模算法调整均衡器,然后根据判决条件自动切换到判决引导算法,最终实现信道的盲均衡.仿真结果表明:此算法收敛速度快、稳态误差小,并能在去除码间干扰的同时纠正信道固有的相位旋转,具有很好的实用性.     

多尺度变步长最小均方自适应算法在光纤陀螺数据处理中的应用

为有效抑制光纤陀螺(FOG)随机噪声,提出将一种多尺度变步长最小均方(MVSLMS)自适应算法应用于FOG数据处理中。根据FOG输出数据的特点,构建了MVSLMS自适应滤波器,提出了FOG信号滤波算法的实现步骤。对FOG实测静态数据、振动数据和速率测试数据进行了滤波实验,结果表明所提算法对FOG随机噪声的抑制效果明显,相比LMS滤波,MVSLMS自适应滤波后的静态数据零偏稳定性数值减小了72.0%,振动数据在振前、振中、振后零偏稳定性数值分别减小91.5%,77.4%和96.5%,速率测试数据标准差减小了54.4%。摇摆测试滤波实验结果表明所用算法对FOG真值信号具有较好的跟踪能力。     

Adaptive step-size modified fractional least mean square algorithm for chaotic time series prediction

This paper presents an adaptive step-size modified fractional least mean square （AMFLMS） algorithm to deal with a nonlinear time series prediction. Here we incorporate adaptive gain parameters in the weight adaptation equation of the original MFLMS algorithm and also introduce a mechanism to adjust the order of the fractional derivative adaptively through a gradient-based approach. This approach permits an interesting achievement towards the performance of the filter in terms of handling nonlinear problems and it achieves less computational burden by avoiding the manual selection of adjustable parameters. We call this new algorithm the AMFLMS algorithm. The predictive performance for the nonlinear chaotic Mackey Glass and Lorenz time series was observed and evaluated using the classical LMS, Kernel LMS, MFLMS, and the AMFLMS filters. The simulation results for the Mackey glass time series, both without and with noise, confirm an improvement in terms of mean square error for the proposed algorithm. Its performance is also validated through the prediction of complex Lorenz series.