大型实对称矩阵分块迭代求逆算法

为提高大型实对称矩阵数值求逆算法的运行速度,设计了一种分块迭代求逆算法,对算法做了详细的理论推导与分析。实现了四种常见的数值求逆算法,即Jacobi数值方法、QR分解法、LU分解法和高斯-约旦法,并分别与分块迭代求逆算法进行了对比分析。实验结果表明,在保证算法精度的情况下,分块迭代求逆算法极大的提高了算法的运行速度。当计算大小为700x700的实对称矩阵的逆矩阵时,相对于LU分解法,加速比为4倍;相对于QR分解法,加速比为26倍。     

基于双向抛物方程逆算法的障碍物定位技术研究

在单向抛物方程法的逆算法即逆绕射抛物方程法的基础上,研究了双向抛物方程法的逆算法,对障碍物引起的后向场进行了逆绕射运算,并根据该逆绕射场分布来确定障碍物的位置和高度.基于此逆算法对刃峰形障碍物的定位问题进行了数值仿真求解,分析了天线高度和刃峰位置的变化对定位精度的影响.数值结果表明双向抛物方程逆算法能用于对障碍物进行定位,且对单刃峰的定位精度很高.     

特征值平移超分辨率算法

本文分析了逆幂迭代算法的收敛速度。为了克服逆幂迭代算法矩阵求逆的过程,提出了特征值平移幂迭代算法,并分析了它的收敛速度,给出了计算机模拟结果。     

基于GF(2~m)的椭圆曲线求逆算法的改进研究

针对二进制域上现有求逆算法计算量大、并行度小、速度慢的缺点进行改进,基于二元Euclidean算法提出了改进,设计了相应的乘法器硬件结构,并且分析了其运算效能和资源占用情况。将此求逆计算器的并行改进算法使用Verilog语言编程实现,利用Xilinx ISE 12.4对整个求逆算法综合仿真(行为级),在Xilinx Virtex-5 XC5VFX70T的硬件平台上验证求逆算法的运算效率,结果表明对求逆算法的改进有效地提高了求逆运算的速度。     

大气波导中基于抛物方程法的障碍物定位

为了解决遥感遥测、自动导航、电子战等领域中常常涉及到障碍物定位问题,在单向抛物方程法的逆算法的基础上提出了双向抛物方程法的逆算法。应用该逆算法对大气波导环境中的障碍物进行了探测和定位,采用插值法分析了大气波导对障碍物定位精确度的影响。仿真结果表明,通过双向抛物方程法的逆算法可以对传播路径上的障碍物进行探测和定位,而且大气折射率变化会对定位精确度产生较大影响。     

特征值平移超分辨率算法

本文分析了逆幂迭代算法的收敛速度，为了克服逆幂迭代算法矩阵求逆的过程，提出了特征平幂迭代算法，并分析了它的收敛速度，给出了计算机模拟结果。     

基于脉动阵的自适应波束形成算法仿真

为了满足自适应波束形成技术对实时性的要求,将脉动阵应用于波束形成算法中,以实现自适应权向量的实时获取.通过对QR分解采样矩阵求逆算法、混合型QR分解采样矩阵求逆算法和逆QR分解采样矩阵求逆算法的理论分析,利用脉动阵,对输入数据矩阵进行QR分解,得到旋转因子,实现自适应权向量的实时更新.针对三种算法各自的脉动阵进行仿真,结果表明,基于脉动阵的自适应波束形成算法实现了权向量的高速获取,并为后续的工程实践提供了理论依据.     

基于回溯法的逆推攻击

该文针对基于乘法电路的前馈序列密码模型的分析问题,在Golic逆推攻击算法的基础上,利用回溯法及前馈函数的输入输出相关性,提出了基于回溯法的逆推攻击算法。在解决了回溯法平均计算复杂性的基础上,给出了基于回溯法的逆推攻击算法的平均计算复杂性。新的逆推攻击算法在存储复杂性和平均计算复杂性方面均优于Golic算法。     

基于人类视觉系统模型的彩色图像逆半调算法

针对彩色图像逆半调算法普遍存在破坏色彩相关性和复杂度较高的缺点,提出了一种基于人类视觉系统模型的逆半调算法.该算法将人类视觉系统处理彩色图像的方法应用到彩色图像逆半调系统中,按照人类视觉系统模型和特征选取了逆半调处理的彩色空间和滤波器,设计了滤波器的形状和尺寸.为了验证算法的有效性,将该算法与查找表逆半调算法、小波逆半调算法进行了比较.仿真实验表明,该算法的逆半调图像质量较好且计算复杂度较低.     

基于空时联合抗干扰算法的复矩阵求逆

针对卫星导航接收机易被干扰的特点,研究了空时联合抗干扰算法,并提出了一种简化的复正定厄米矩阵求逆实现方法。首先,给出了空时联合抗干扰算法的基本模型。随后,在详细分析传统复正定厄米矩阵求逆算法基础上,给出了改进的复正定厄米矩阵求逆方法的具体步骤。最后,基于数字信号处理（DSP）的硬件平台,对运算量进行了对比分析。仿真结果表明,改进的复正定厄米矩阵求逆方法加法和乘法运算量都大幅度降低,比传统算法运算速度加快了三分之一。算法简单可靠,易于实现,适用于工程应用中实时性要求较高的场合。     

一种应用于多通道自适应有源控制的快速算法

主要讨论了应用于多通道有源噪声控制系统的自适应组合逆算法及其性能分析。通过对该算法的理论推导,阐明其整体思路及物理意义,同时通过在同参数条件下与FxLMS算法的计算机仿真比较,得出结论:在多通道有源控制中,组合逆算法有效地降低了算法运算量,适合应用于多通道系统。     

自适应增益LMS的盲多用户检测算法

研究了一种工作于CDMA系统的盲多用户检测算法--自适应增益LMS,推导了算法实现的步骤.仿真实验表明,该算法相对于传统的LMS盲多用户检测算法,大幅度提高了收敛速率和稳态输出信干比,大大增强了适应非平稳信道的能力.另外,该算法运算量远远低于多用户检测中的盲RLS和Kalman滤波算法,非常适合于硬件实现.     

A fast frequency-domain adaptive algorithm

A time-varying convergence factor μ is proposed for the frequency-domain LMS (least-mean-square) adaptive algorithm, which results in the frequency-domain optimal block algorithm (FOBA). The FOBA is the frequency-domain implementation of the recently proposed optimum block algorithm (OBA). The FOBA results in computational savings in comparison to the OBA and in performance enhancement relative to the frequency-domain LMS algorithm     

Simplified Newton-type adaptive estimation algorithms

A new adaptive estimation algorithm is presented. It is the result of a combination of the LMS and the fast Newton transversal filters (FNTF) class. The main characteristic of the proposed algorithm is its improved convergence rate as compared to LMS, for cases where it is known that LMS behaves poorly. This improved characteristic is achieved in expense of a slight increase in the computational complexity while the overall algorithmic structure is very simple (LMS type). The proposed algorithm seems also to compare relatively well against RLS and FNTF     

LMS和归一化LMS算法收敛门限与步长的确定

从LMS算法失调量的准确表达式出发,根据输入信号特征值分布重新研究了LMS,归一化LMS(Normalized LMS,NLMS)算法收敛的必要条件,推导出LMS和NLMS 算法收敛的步长门限,并分析了输入信号特征值分布、滤波器阶数对算法收敛步长门限的影响,推导出满足性能失调下步长的自适应计算公式,减小了应用 LMS,NLMS算法时步长选取的盲目性,与已有的算法相比,具有计算简单、实用、自适应性能强,同时可获得满意失调量的特点,计算机模拟结果表明该方法的正确性。     

Line search algorithms for adaptive filtering

Line search algorithms for adaptive filtering that choose the convergence parameter so that the updated filter vector minimizes the sum of squared errors on a linear manifold are described. A shift invariant property of the sample covariance matrix is exploited to produce an adaptive filter stochastic line search algorithm for exponentially weighted adaptive equalization requiring 3N+5 multiplications and divisions per iteration. This algorithm is found to have better numerical stability than fast transversal filter algorithms for an application requiring steady-state tracking capability similar to that of least-mean square (LMS) algorithms. The algorithm is shown to have faster initial convergence than the LMS algorithm and a well-known variable step size algorithm having similar computational complexity in an adaptive equalization experiment     

改进的变步长LMS改进算法

在基于Lorentzian函数的变步长LMS自适应滤波算法的基础上,进行进一步改进,提出了一种新的自适应LMS滤波算法,通过建立新的误差信号e(n)与变步长因子μ(n)之间的关系,消除不相关噪声的影响。并用Matlab对其进行仿真验证,表明该算法解决了收敛速度和稳态误差之间的矛盾,在保证算法的计算复杂度较低的同时,使得算法的抗干扰能力进一步提高,适用于低信噪比条件下的信号提取及滤波,为实际应用提供了更大的灵活性。     

一种用于水声相干通信系统的自适应均衡算法

多通道判决反馈均衡器(MC-DFE)是水声相干通信克服信道多径效应、消除码间干扰(ISI)的主要手段。为了减少多通道数据处理的复杂性、优化算法、提高算法精度,该文提出了自适应自最优预合并多通道判决反馈均衡算法。该算法将快速自优化LMS分集合并(FOLMSDC)算法、快速自优化LMS(FOLMS)算法和快速自优化LMS相位补偿(FOLMSPC)算法有机地结合在一起,使用统一的误差信号,按照最小化均方误差(MMSE)准则调节各部分的系数,从而实现均衡器性能的全局最优。仿真试验和湖上试验对该算法的性能进行了分析。实验结果表明,该文提出的算法可以进一步减少运算量,提高通信系统的接收性能,算法性能优于已有算法。     

Unbiased and stable leakage-based adaptive filters

The paper develops a leakage-based adaptive algorithm, referred to as circular-leaky, which in addition to solving the drift problem of the classical least mean squares (LMS) adaptive algorithm, it also avoids the bias problem that is created by the standard leaky LMS solution. These two desirable properties of unbiased and bounded estimates are guaranteed by circular-leaky at essentially the same computational cost as LMS. The derivation in the paper relies on results from averaging theory and from Lyapunov stability theory, and the analysis shows that the above properties hold not only in infinite-precision but also in finite-precision arithmetic. The paper further extends the results to a so-called switching-σ algorithm, which is a leakage-based solution used in adaptive control     

Partial update LMS algorithms

Partial updating of LMS filter coefficients is an effective method for reducing computational load and power consumption in adaptive filter implementations. This paper presents an analysis of convergence of the class of Sequential Partial Update LMS algorithms (S-LMS) under various assumptions and shows that divergence can be prevented by scheduling coefficient updates at random, which we call the Stochastic Partial Update LMS algorithm (SPU-LMS). Specifically, under the standard independence assumptions, for wide sense stationary signals, the S-LMS algorithm converges in the mean if the step-size parameter /spl mu/ is in the convergent range of ordinary LMS. Relaxing the independence assumption, it is shown that S-LMS and LMS algorithms have the same sufficient conditions for exponential stability. However, there exist nonstationary signals for which the existing algorithms, S-LMS included, are unstable and do not converge for any value of /spl mu/. On the other hand, under broad conditions, the SPU-LMS algorithm remains stable for nonstationary signals. Expressions for convergence rate and steady-state mean-square error of SPU-LMS are derived. The theoretical results of this paper are validated and compared by simulation through numerical examples.