高速相干光通信系统中的自适应步长恒模算法

对于高速光通信系统中的相干检测来说,盲均衡是一项常用的恢复传输符号的技术。目前应用最广泛的盲均衡算法是恒模算法,但是一方面它不能兼顾收敛速度和收敛精度,另一方面在偏分复用系统中很容易陷入奇异性。因此提出了一种改进型的恒模算法———自适应步长恒模算法,并在112Gb/s偏分复用16进制正交幅度调制系统中对提出的算法进行了测试。与传统的恒模算法相比,改进后的算法收敛速度仅为恒模算法的1/20;收敛稳定后误差函数的方差比恒模算法降低了0.7dB;对偏分复用系统中的奇异性有很好的抵抗能力;在不考虑奇异性问题时,光信噪比代价与恒模算法相比降低了1.5dB。     

超高速相干光通信两步步长优化CMA算法

在超高速相干光通信中，对偏振解复用损伤和偏振模色散的补偿一般采用恒模算法(constant modulus algorithm, CMA)。标准的CMA算法具有自适应动态均衡的功能，加权信号根据输入信号进行不断更新，是一种目前流行的盲均衡算法。通常在CMA算法中，步长系数的取值严重影响着算法收敛、误差矢量幅度(error vector magnitude, EVM)和误码率(bit error ratio, BER)性能评价。提出了一种优化的CMA算法，具有两步步长系数，可标记误差矢量函数曲线稳定时的区间。经过计算验证表明，优化的CMA算法具有不同程度的解调性能改善，能够降低X、Y偏振态的EVM，具备较好的应用性。     

基于量子粒子群优化的正交小波加权多模盲均衡算法

针对常数模盲均衡算法(CMA)均衡高阶正交振幅调制信号(QAM)存在收敛速度慢、稳态误差大的缺点, 提出了基于量子粒子群优化的正交小波加权多模盲均衡算法(QPSO-WTWMMA). 该算法根据高阶QAM信号星座图分布特点, 将量子粒子群优化算法(QPSO) 和正交小波变换融入于加权多模盲均衡算法(WMMA)中. 因而, 利用QPSO对均衡器权向量进行了优化, 利用正交小波变换降低了输入信号的自相关性, 利用WMMA选择了合适的误差模型匹配QAM星座图. 理论分析及水声信道仿真结果表明, QPSO-WTWMMA算法可以获得更快的收敛速度和更低的稳态误差, 在水声通信中具有重要的参考价值.     

改进支持向量机和常数模算法水声信道盲均衡

针对盲均衡算法收敛速度较慢的问题,提出一种结合改进支持向量机和常数模算法的水声信道盲均衡算法。该算法首先利用具有优异小样本学习能力的支持向量机进行盲均衡器权系数初始化,在完成初始化后切换至运算量较小的常数模算法。考虑到支持向量机本身非自适应运算的限制,在时变水声信道条件下利用经典支持向量机获得的均衡器初始权向量与切换后的信道仍然存在失配。因此,本文导出时变条件下的改进支持向量机用于盲均衡器初始化,改善算法切换时的权系数失配,并结合分数间隔结构和内嵌数字锁相环进一步提高盲均衡算法性能。仿真和湖试实验结果表明:在时变水声信道条件下,本文算法的收敛性能优于经典支持向量机盲均衡算法。      

并联滤波的常数模与判决引导融合盲均衡算法

针对常数模和判决引导双模式盲均衡算法切换时机选择困难问题,提出了一种并联滤波的双模式融合盲均衡算法。算法以并联滤波器作为盲均衡器,两路子滤波器分别以常数模算法准则和判决引导算法准则进行更新,通过加权因子实现两种算法模式自适应切换,完成两种算法的融合处理,加权因子依据归一化均方误差进行调整。为防止信道突发干扰,定义了归一化均方误差信息熵增量,以信息熵增量为判据适时实现均衡器和加权因子重置,使算法在信道突发干扰条件下能实现自适应跟踪。计算机仿真和水池试验处理结果表明:该算法有效地结合了常数模和判决引导算法的优点,具有较好的均衡性能。      

A constant modulus algorithm for blind equalization in α-stable noise

Channel noise is often assumed to be Gaussian in most of the existing channel equalization algorithms. The performance of these algorithms will degrade seriously when the noise is non-Gaussian. This paper deals with the problem of blind channel equalization in impulsive noise environment that is modeled as α-stable process. A modified adaptive error-constrained constant modulus algorithm (MAECCMA) is proposed by soft-limiting the amplitude of the equalizer input and transforming the error signal of the original adaptive error-constrained constant modulus algorithm (AECCMA) nonlinearly to suppress the influence of α-stable noise. Computer simulation results of two underwater acoustic channels show that, MAECCMA has almost the same performance as AECCMA and they both have faster convergence rate than constant modulus algorithm (CMA) and normalized least mean absolute deviation (NLMAD) algorithm in Gaussian noise, while MAECCMA provides the best performance of those four algorithms in α-stable noise.     

Non-negative infrared patch-image model: Robust target-background separation via partial sum minimization of singular values

To further enhance the small targets and suppress the heavy clutters simultaneously, a robust non-negative infrared patch-image model via partial sum minimization of singular values is proposed. First, the intrinsic reason behind the undesirable performance of the state-of-the-art infrared patch-image (IPI) model when facing extremely complex backgrounds is analyzed. We point out that it lies in the mismatching of IPI model’s implicit assumption of a large number of observations with the reality of deficient observations of strong edges. To fix this problem, instead of the nuclear norm, we adopt the partial sum of singular values to constrain the low-rank background patch-image, which could provide a more accurate background estimation and almost eliminate all the salient residuals in the decomposed target image. In addition, considering the fact that the infrared small target is always brighter than its adjacent background, we propose an additional non-negative constraint to the sparse target patch-image, which could not only wipe off more undesirable components ulteriorly but also accelerate the convergence rate. Finally, an algorithm based on inexact augmented Lagrange multiplier method is developed to solve the proposed model. A large number of experiments are conducted demonstrating that the proposed model has a significant improvement over the other nine competitive methods in terms of both clutter suppressing performance and convergence rate.     

Flexible polarization demultiplexing method based on an adaptive process noise covariance Kalman filter

A flexible polarization demultiplexing method based on an adaptive Kalman filter(AKF) is proposed in which the process noise covariance has been estimated adaptively. The proposed method may significantly improve the adaptive capability of an extended Kalman filter(EKF) by adaptively estimating the unknown process noise covariance. Compared to the conventional EKF, the proposed method can avoid the tedious and time consuming parameter-by-parameter tuning operations. The effectiveness of this method is confirmed experimentally in 128 Gb/s 16 QAM polarization-division-multiplexing(PDM) coherent optical transmission systems. The results illustrate that our proposed AKF has a better tracking accuracy and a faster convergence(about 4 times quicker)compared to a conventional algorithm with optimal process noise covariance.     

基于遗传算法的锂离子电池等效电路参数识别

结合锂离子电池双极性等效电路模型提出了一种基于遗传算法的参数识别方法,该方法通过指数函数对电路模型中的电阻、电容、恒压源等元件进行有理逼近,根据电池在不同充放电速率下的输出电压特性数据,通过实数编码遗传算法得到最优的函数参数,从而得到最优的电阻、电容,开路电压等电路参数值,针对电池在不同的工作状态,不同的工作参数下的运行数据,系列仿真和实验结果表明该算法原理简明,收敛较快,辨识得到的最优模型其电压输出特性与电池的实际电压输出特性基本吻合,能较精确的反映电池的实际特性,具有较高的辨识精度。     

模拟退火算法在无端偏振控制器中的应用

构造了由四个相移范围在0~2π的电光晶体波片组成的偏振控制器,利用模拟退火法作为其反馈控制算法.仿真计算表明,该偏振控制器是无端的,在较优的控制参量下,其输出的偏振态是稳定的,光强波动小于1%.对于四波片偏振控制器,模拟退火算法的控制参量研究表明,为得到较佳的控制效果,可把内外循环次数各限制为3次,以减少处理器时间;其初温T0应取在0.1附近;温度更改常数p宜取在0.2左右;状态产生常数s对输出的稳定性影响最大,仿真说明设在 0.005~0.01间为好.