基于FPGA的光纤陀螺自适应LMS滤波算法研究

光纤陀螺仪的精度严重制约着以其为核心的惯性导航系统精度,为进一步提高系统精度须对光纤陀螺进行滤波处理。本文在自适应LMS滤波算法的基础上,以FPGA实现光纤陀螺数据的采集和滤波处理。以光纤陀螺的延时信号作为参考值,实时自适应调整陀螺不同时刻输出的权值来实现对噪声的滤除。在FPGA中,利用有限状态机的方式来完成该滤波算法的实现,同时完成权值系数的更新和信号采集等模块的功能。结果表明,经过FPGA采集并滤波后的光纤陀螺信号中的噪声分量明显降低,滤波延时远小于采样周期,完全满足工程实际需要。     

基于随机并行梯度下降算法的自适应光学实时并行处理机

基于随机并行梯度下降(SPGD)算法的自适应光学系统通过直接优化系统的性能评价函数来控制波前校正器以补偿光束中存在的波前畸变。但由于算法收敛速度的影响,在一定程度上限制了SPGD在自适应光学系统中的应用。在对SPGD控制算法分析的基础上,充分提取和发掘算法内在的并发性,采用流水线和并行处理技术,设计并实现了基于现场可编程门阵列(FPGA)加数字信号处理器(DSP)的单指令流多数据流(SIMD)结构实时并行处理机,实现了SPGD控制算法由表达层到结构层的优化映射。该处理机应用在激光光束净化自适应光学系统中,同时实现了对变形镜和倾斜镜的控制。实验结果表明,采用基于SPGD算法自适应光学实时并行波前处理机具有很快的收敛速度,可以有效地校正激光出光过程中的光束波前相差和光束漂移误差。     

基于GPU的多帧盲解卷积图像复原技术并行化实现

多帧盲解卷积图像复原技术能够进一步提高自适应光学图像的分辨力,但其算法比较复杂,处理耗时过长,对序列图像复原经常需要几分钟甚至几十分钟的计算时间,对实际应用造成了极大不便。为了提升算法的运行速度,改善其耗时过长的问题,通过研究和分析盲解卷积算法原理和算法结构,采用目前高速发展的中央处理器（CPU）和图形处理器（GPU）异构加速技术,主要对耗时最长的矩阵卷积运算进行优化,通过使用库函数与算法结构微调相结合的方法并行加速,实现多帧盲解卷积的图像复原算法的并行化。使用并行算法对图像进行复原处理,针对16帧以上分辨率为256256像素的空间目标图像,可以实现17的加速比,为图像复原的实时/准实时提供一种可行的方案。     

航天遥感相机星上自适应图像增强研究与实验

为了解决航天遥感图像在地面增强处理带来的滞后性和失真性,因此提出了星上自适应图像增强的方法和一种新的基于自适应线性拉伸的拉普拉斯滤波算法,来实现星上图像增强处理的实时性和自适应性;在拉普拉斯滤波的基础上,用自适应调整参数A和B对图像的对比度和边缘进行增强,该算法结构简单,运算量小;最后建立以FPGA为核心的硬件系统平台,采用流水线的处理方式,对该算法进行实验验证;实验结果表明,与其他增强算法相比,文章算法增强的图像目视效果更好,信息熵提高了10.21%,处理一幅图像所需的时间是79.6 ms,满足航天遥感相机星上自适应图像增强实时性的要求,达到了预期效果。     

基于多结构元素形态滤波与自适应阈值分割相结合的红外弱小目标检测

针对低信噪比灰度图像中弱小目标检测的难题,分析了红外弱小目标成像的特点,提出了基于多结构元素形态滤波与自适应阈值分割相结合的目标检测算法.利用目标运动的连续性、规律性和噪音产生的随机性,结合数学形态学结构元素的特点,研究了一种多结构元素形态滤波的管道滤波方法,通过流水线管道检测目标运动轨迹.实验结果表明,该算法应用于复...     

超指数判决反馈水声信道盲均衡算法实验研究

利用超指数算法收敛速度快的优点,对判决反馈盲均衡算法的前向权向量的迭代式进行了修正,提出了一种新的判决反馈盲均衡算法。该算法具有收敛速度快,跟踪性能好等特点。通过水池实验,获得了符合盲均衡算法统计特性的BPSK数据,对所提出的算法与自适应判决反馈均衡算法,常数模盲均衡算法等进行了对比分析。结果表明,该算法可以有效的降低误码率,且收敛速度要优于其他算法,可靠实现了对多途水声信道的均衡。     

自适应光学图像非对称图像迭代盲复原算法

 为了提高自适应光学图像复原效果,提出了一种新的多重约束非对称图像迭代盲解卷积算法。首先,在点扩散函数（PSF）频率域引入带宽有限约束来提高迭代盲解卷积算法的可靠性;然后,在PSF空间域引入支持域动态更新的思想以加快迭代盲解卷积算法收敛速度;最后,自动计算迭代盲解卷积算法的非对称因子以提高算法的自适应性。模拟实验结果表明,与RL-IBD算法比较,新算法迭代次数减少22.4%、峰值信噪比提高10.18 dB。在FK5-857和某双星的自适应光学图像复原实验中,也取得很好的复原效果。     

二频机抖激光陀螺高精度低延时信号解调电路设计

在激光陀螺信号解调滤波过程中,工程上经常运用高阶FIR滤波电路来实现抖动信号剥除;这种方法虽然精度高但具有较高的延时,无法满足激光陀螺输出结果实时性的要求;为了解决这个问题,提出了一种基于FPGA的激光陀螺信号解调电路,利用自适应滤波原理对激光陀螺输出抖动信号进行自适应剥除,最后进行了相应精度及延迟测试;实验结果表明激光陀螺信号经过该系统自适应抖动剥除后效果较好,激光陀螺静态输出百秒方差仅为千分之三且系统延迟0.6 ms远低于常规5 ms延迟,满足了激光陀螺信号解调高精度低延时的要求。     

稳健的双模型自适应切换实时跟踪算法

为提升卷积特征目标跟踪算法的实时性和稳健性,利用不同卷积层特征对不同目标表征能力不同的特性,提出双模型自适应切换的实时跟踪方法。该方法对选取的两个卷积层特征使用目标区域和跟踪搜索区域卷积特征的能量均值比来评估卷积特征,选择能量均值比大于给定阈值的卷积通道特征来训练两个相关滤波分类器,然后利用目标相关滤波响应图的峰旁比自适应切换两个相关滤波分类器来预测目标位置,最后采用稀疏模型更新策略来更新分类器。在标准数据集上进行算法测试,实验结果表明,本文算法平均距离精度为89.3%,接近连续卷积跟踪算法,平均跟踪速度为25.8frams/s,是连续卷积跟踪算法的25倍,整体性能优于实验中的对比跟踪算法。     

自适应滤波器在机载武器测试系统中的应用

针对飞机机载武器设备特点和测试维修要求,在分析了自适应滤波器的结构、最优评估等特性基础上,根据现场可编程门阵列(FPGA)的设计过程,构建了基于最小均方误差(LMS)算法的横向自适应滤波器.仿真结果和使用表明:设计实现的自适应滤波器,在FPGA中开辟512 kB的随机存储器(RAM)存储空间,能够实现权值系数向量的自动调节,且适应信号变化的特性,滤波后的信号测试误差不大于5%,且LMS算法在0.1 ms内可收敛到最优,满足数据处理的实时性和机载武器系统测试的要求.     

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.     

语音通信降噪研究

语音通信系统中，语音通过信道传输将不可避免地引入码间串扰和信号畸变，同时受到噪声污染。本文在分析自适应盲均衡算法CMA(constant modulus algorithm）和改进盲均衡算法的基础上，考虑到自适应盲均衡技术在语音噪声控制方面能力有限，将自适应盲均衡技术与小波包掩蔽阈值降噪算法联合使用，形成一种基带语音增强新方法。仿真试验结果显示自适应盲均衡技术可以使星座图变得清晰而紧凑，有效减小误码率。研究证实该方法在语音信号ISI和畸变严重情况下，在白噪及有色噪声不同的噪声环境中都具有稳定的降噪能力，消噪同时可获得汉语普通话良好的听觉效果。     

Efficient processing of acoustic signals for high-rate information transmission over sparse underwater channels

For underwater acoustic channels where multipath spread is measured in tens of symbol intervals at high transmission rates, multichannel equalization required for bandwidth-efficient communications may become prohibitively complex for real-time implementation. To reduce computational complexity of signal processing and improve performance of data detection, receiver structures that are matched to the physical channel characteristics are investigated. A decision-feedback equalizer is designed which relies on an adaptive channel estimator to compute its parameters. The channel estimate is reduced in size by selecting only the significant components, whose delay span is often much shorter than the multipath spread of the channel. Optimal coefficient selection (sparsing) is performed by truncation in magnitude. This estimate is used to cancel the post-cursor ISI prior to linear equalization. Spatial diversity gain is achieved by a reduced-complexity pre-combining method which eliminates the need for a separate channel estimator/equalizer for each array element. The advantages of this approach are reduction in the number of receiver parameters, optimal implementation of sparse feedback, and efficient parallel implementation of adaptive algorithms for the pre-combiner, the fractionally-spaced channel estimators and the short feedforward equalizer filters. Receiver algorithm is applied to real data transmitted at 10 kbps over 3 km in shallow water, showing excellent results.     

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.     

A multichannel and multiple position adaptive room response equalizer in warped domain: Real-time implementation and performance evaluation

Room response equalization systems are used for improving the listening experience in cinema theatres, home theatres, car hi-fi systems. In this paper, an adaptive multichannel and multiple position room response equalization system and its real-time implementation are described. An adaptive and accurate estimation of the room responses is provided introducing a normalized least mean square optimization approach with a variable step-size, and taking advantage of an interchannel coherence reduction technique based on the missing fundamental phenomenon. Then, the equalizer is designed in warp frequency domain for improving equalization in the low frequency region, reducing the computational cost of the design procedure, and deriving an algorithm capable of working in real time. Indeed, a real-time implementation of the proposed adaptive equalizer has been obtained on NU-Tech framework and has been used in order to provide a deep objective and subjective evaluation of the equalization system. The results of these evaluations illustrate the effectiveness of the proposed approach, also in comparison with other techniques of the state of the art.     

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.     

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.     

Adaptive and efficient nonlinear channel equalization for underwater acoustic communication

We investigate underwater acoustic (UWA) channel equalization and introduce hierarchical and adaptive nonlinear (piecewise linear) channel equalization algorithms that are highly efficient and provide significantly improved bit error rate (BER) performance. Due to the high complexity of conventional nonlinear equalizers and poor performance of linear ones, to equalize highly difficult underwater acoustic channels, we employ piecewise linear equalizers. However, in order to achieve the performance of the best piecewise linear model, we use a tree structure to hierarchically partition the space of the received signal. Furthermore, the equalization algorithm should be completely adaptive, since due to the highly non-stationary nature of the underwater medium, the optimal mean squared error (MSE) equalizer as well as the best piecewise linear equalizer changes in time. To this end, we introduce an adaptive piecewise linear equalization algorithm that not only adapts the linear equalizer at each region but also learns the complete hierarchical structure with a computational complexity only polynomial in the number of nodes of the tree. Furthermore, our algorithm is constructed to directly minimize the final squared error without introducing any ad-hoc parameters. We demonstrate the performance of our algorithms through highly realistic experiments performed on practical field data as well as accurately simulated underwater acoustic channels.     

Robust Blind Adaptive Channel Equalization in Chaotic Communication Systems

Based on the bounded property and statistics of chaotic signal and the idea of set-membership identification, we propose a set-membership generalized least mean square （SM-GLMS） algorithm with variable step size for blind adaptive channel equalization in chaotic communication systems. The steady state performance of the proposed SM-GLMS algorithm is analysed, and comparison with an extended Kalman filter （EKF）-based adaptive algorithm and variable gain least mean square （VG-LMS） algorithm is performed for blind adaptive channel equalization. Simulations show that the proposed SM-GLMS algorithm can provide more significant steady state performance improvement than the EKF-based adaptive algorithm and VG-LMS algorithm.     

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。