Property of the Constant Modulus Algorithm to Capture the Weak Signal

Property of the Constant Modulus Algorithm to Capture the Weak Signal~~     

The Least Square Constant Modulus Algorithm for Blind Adaptive Multiuser Detection

1　ＩｎｔｒｏｄｕｃｔｉｏｎＭｕｌｔｉｕｓｅｒＤｅｔｅｃｔｉｏｎ (ＭＵＤ)ｉｓｍａｉｎｌｙａｐｐｌｉｅｄｉｎＣＤＭＡｔｏｃａｎｃｅｌｔｈｅｍｕｌｔｉａｃｃｅｓｓｉｎｔｅｒｆｅｒｅｎｃｅ.Ｔｈｅｓｉｎｇｌｅ ｕｓｅｒｍａｔｃｈｅｄｆｉｌｔｅｒｏｒｒａｋｅｒｅｃｅｉｖｅｒｔｒｅａｔｓｔｈｅｉｎｔｅｒｆｅｒｅｎｃｅｆｒｏｍｏｔｈｅｒｕｓｅｒｓａｓｎｏｉｓｅ ,ｂｕｔＭＵＤｔａｋｅｓａｄｖａｎｔａｇｅｏｆｔｈｅｏｔｈｅｒｕｓｅｒ’ｓｉｎｔｅｒｆｅｒｅｎｃｅ[1 ].ＢｌｉｎｄａｄａｐｔｉｖｅＭＵＤｈａｓｂｅｅｎｔ…     

基于小波分析和鲁棒最小二乘的信息融合估计算法

对于农田土壤的多传感器检测系统来说,其测量的信息具有非线性和时空变异等特性,因此对信息融合方法的基本要求是具有鲁棒性和并行处理能力.应用小波分析理论,对原始测量数据进行了降噪处理,使降噪后的数据更能反映土壤的本质及变化规律:应用鲁棒最小二乘估计技术可以对不同传感器数据进行综合处理,去除冗余,克服歧义,得到比任何单个传感器更全面、更准确、更可靠的信息.针对农业中的自然环境具有很强的不确定性和经验性,运用基于小波分析的降噪和现代信息融合思想,提出了一种基于小波降噪和鲁棒最小二乘的信息融合估计方法.通过实验分析,其结果表明该方法是可行和值得研究的.     

Constrained Weighted Least Squares Location Algorithm Using Received Signal Strength Measurements

Determine the location of a target has gained considerable interest over the past few years. The Received Signal Strength(RSS) measurements and Differential RSS(DRSS) measurements can be converted to distance or distance ratio estimates for constructing a set of linear equations. Based on these linear equations, a constrained weighted least Squares(CWLS) algorithm for target localization is derived. In addition, an iterative technique based on Newton’s method is utilized to give a solution. The covariance and bias of the CWLS algorithm is derived using perturbation analysis. Simulation shows that the proposed estimator achieves better performance than existing algorithms with reasonable complexity.     

ADSP Tiger SHARC 101S在基于最小二乘恒模算法中的应用

对ADSP Tiger SHARC 101S的在最小二乘恒模算法中的应用进行了研究。首先介绍了最小二乘恒模算法基本原理和权值迭代公式，然后介绍了ADSP Tiger SHARC 101S芯片的基本结构特征和他强大的功能，并简单描述了其相应开发软件Visual DSP^ 3．0的功能及使用方法．给出了最小二乘恒模算法实现波束合成的具体流程图，在经过Matlab软件成功进行仿真模拟之后用Visual DSP^ 3.0设计了DSP程序，用C语言嵌套部分汇鳊语言的形式实现了用ADSP Tiger SHARC 101S进行迭代求权向量的过程。     

基于LS_SVM的下行波束盲校正算法

在智能天线系统中,由于各阵元之间存在着耦合和激励误差,在对下行波束进行赋形时,这些误差会使产生波束指向严重偏离期望的方向,造成天线增益下降。针对这一问题,提出了一种基于最小二乘支持矢量机（LS_SVM）的下行波束盲校正算法：该算法首先利用发射的训练序列对支持矢量机（SVM）进行训练,然后利用所得的权值补偿耦合和激励误差,使发射波束的指向符合期望的方向,最后采用自适应波束综合算法产生主波束宽度以及副瓣电平符合期望值的方向图。对该方法进行了仿真验证,仿真结果表明,该算法简单易行,具有实时处理的能力。     

基于最小二乘隐空间支持向量机的IDS检测算法的设计

在基于支持向量机的基础上,提出一种新的利用最小二乘隐空间支持向量机设计IDS的检测算法,解决了网络入侵检测系统中检测算法的分类精度不高、训练样本数需要较多,及训练学习时间较长等问题.仿真实验结果表明,本算法较基于支持向量机的检测算法具有更快的收敛性、更快的迭代速度、更高的检测精度和更低的误报率.     

Optimal Step-Size Constant Modulus Algorithm

The step size leading to the absolute minimum of the constant modulus (CM) criterion along the search direction can be obtained algebraically at each iteration among the roots of a third-degree polynomial. The resulting optimal step-size CMA (OS-CMA) is compared with, other CM-based iterative techniques in terms of performance-versus-complexity trade-off.     

一种基于分数间隔的判决变步长恒模算法

为了解决盲均衡技术中恒模算法收敛速度和收敛精度与步长矛盾的问题,在引入反馈因子的同时提出了一种反馈变步长恒模算法。通过仿真分析表明,该算法比传统的恒模算法收敛速率更快、稳定性更好。为了进一步降低短波信道中存在的码间串扰,将分数间隔均衡器与盲均衡相结合,提出了一种基于分数间隔的反馈变步长恒模算法。相比其它算法而言,该算法可明显改善均方误差性能,具有重要的实际意义。     

Extended Kernel Recursive Least Squares Algorithm

This paper presents a kernelized version of the extended recursive least squares (EX-KRLS) algorithm which implements for the first time a general linear state model in reproducing kernel Hilbert spaces (RKHS), or equivalently a general nonlinear state model in the input space. The center piece of this development is a reformulation of the well known extended recursive least squares (EX-RLS) algorithm in RKHS which only requires inner product operations between input vectors, thus enabling the application of the kernel property (commonly known as the kernel trick). The first part of the paper presents a set of theorems that shows the generality of the approach. The EX-KRLS is preferable to 1) a standard kernel recursive least squares (KRLS) in applications that require tracking the state-vector of general linear state-space models in the kernel space, or 2) an EX-RLS when the application requires a nonlinear observation and state models. The second part of the paper compares the EX-KRLS in nonlinear Rayleigh multipath channel tracking and in Lorenz system modeling problem. We show that the proposed algorithm is able to outperform the standard KRLS and EX-RLS in both simulations.     

A Robust Least Mean Square Algorithm for Adaptive Array Signal Processing

Least Mean Square (LMS) has been the most popular scheme in the realization of adaptive beamforming algorithms. In this paper a Robust Least Mean Square (R-LMS) algorithm is proposed which uses ratio parameters to control the contribution of product vectors in the weight upgrading process. The idea behind the proposed scheme is inclusion of previous information in place of relying solely on current sample. The performance enhancement by R-LMS algorithm is achieved with insignificant increase in computational complexity of LMS algorithm, so the crux of the conventional technique is not lost. Simulation results are also presented which illustrate that R-LMS provides relatively fast convergence, less Brownian motion and improved stability.     

A New Weighted Least Squares Support Vector Machines and Its Sequential Minimal Optimization Algorithm

Least squares support vector machines （LSSVM） is widely used in pattern recognition and artificial intelligence domain in recent years for its efficiency in classification and regression. The solution of LSSVM is an optimization problem of a Sum squared error （SSE） cost function with only equality constraints and can be solved in a simple linear system. However, its generalization performance is sensitive to noise points and outliers that are often existent in training dataset. In order to endow robustness to LSSVM, a new method for computing weight vector of error is proposed and the substituting of weighted error vector for original error vector in LSSVM gives birth to a new weighted LSSVM. The method gets weight factor by computing distance between sample and its corresponding class center. Sequential minimal optimization （SMO） algorithm is also extended to the new method for its efficient application. Comparison experiments show superiority of the new method in terms of generalization performance, robust property and sparse approximation. Especially, the new method is much faster than the other method for large number of samples.     

一种改进的波束形成算法

论文分析研究了最陡下降恒模算法（SDCMA）在波束形成应用中收敛稳定性好和最小二乘恒模算法（LSCMA）收敛速度快的特点,将两种算法以新的方式相结合,吸收二者的优点,提出了一种基于预解扩判决反馈盲波束形成组合算法（SD—LSCMA）。通过和传统算法在相同环境下进行MATLAB仿真比较,结果表明,文中提出的算法具有更好的抗强多址干扰性能。     

恒模算法:进展与展望

本文阐述了恒模算法的数学模型和基本原理,对近年来出现的各种恒模算法进行了综述,简要分析了这些算法的工作原理和收敛特性。在此基础上讨论了恒模算法在盲均衡、盲多用户检测、干扰抑制和盲波束形成等领域的应用。最后对恒模算法的发展趋势进行了展望。     

迭代更新导向矢量的稳健波束形成算法

稳健的Capon波束形成器(RCB)可以克服常规波束形成对导向矢量误差的敏感性,但其性能受导向矢量误差范围估计值的影响.为此,提出了一种不需要给定误差范围的稳健波束形成方法,利用导向矢量误差中与假想导向矢量正交的分量,将双约束RCB算法的优化问题转换为几个迭代的二次锥规划问题,逐次更新导向矢量以获得最优权,计算机仿真验证了方法的有效性.     

基于改进的共轭梯度算法实现的最小二乘隐空间支持向量机

本文研究了最小二乘隐空间支持向量机的优化问题．文中采用基于对称超松弛预处理技术改进共轭梯度算法,改进的共轭梯度算法只需求解一个阶数为1-1的线性代数方程组即可,大大节省了计算时间．最后将其应用于最小二乘隐空间支持向量机中建立数学模型,并通过实例验证了该算法的优越性．     

适用于相位调制的改进恒模算法

恒模算法(CAM)实现盲均衡直接利用接收信号本身的先验信息对信道进行均衡,避免采用“训练序列”,提高了有效信息率.但该算法也存在着不足之处.对于MPSK、MQAM等相位调制方式来说,在相位偏转信道中散点图存在相位偏转;与带有“训练序列”的均衡模式相比该算法输出信号均方误差大,收敛速度慢.改进的算法通过在代价函数中加入相位信息,较好解决了散点图相位偏转问题;将输出信号判决值引入误差计算装置,有效降低了输出信号的均方误差,并在一定程度上提高了收敛速度.     

杜鹃鸟搜索算法优化最小二乘支持向量机的网络入侵检测模型

为了提高网络入侵检测率,提出一种改进杜鹃鸟搜索（MCS）算法优化最小二乘支持向量机（LSSVM ）的入侵检测模型（MCS-LSSVM ）。首先将LSSVM 的参数作为杜鹃鸟的鸟巢位置,然后通过模拟杜鹃鸟种群寄生繁衍机制,找到鸟巢最优位置,并转化成LSSVM 最优参数,最后采用最优参数建立最优建立入侵检测模型,并采用KDD CUP 99数据集进行仿真测试。仿真结果表明,MCS-LSSVM 不仅提高了网络入侵的检测率,而且减少了训练时间,提高网络入侵检测效果。     

常模类盲均衡算法的研究

讨论了常模类自适应盲均衡技术,总结了改进和设计常模类盲均衡算法的思路,在常模算法代价函数的基础上给出了一种变步长多模算法.计算机仿真表明,此算法收敛速度较快、收敛后稳态误差较小,具有很好的实用性.     

A Supervised Constant Modulus Algorithm for Blind Equalization

Blind equalization is a technique for adaptive equalization of a communication channel without the aid of the usual training sequence. Although the Constant Modulus Algorithm (CMA) is one of the most popular adaptive blind equalization algorithms, it suffers from slow convergence rate. A novel enhanced blind equalization technique based on a supervised CMA (S-CMA) is proposed in this paper. The technique is employed to initialize the coefficients of a linear transversal equalizer (LTE) filter in order to provide a fast startup for blind training. It also presents a computational study and simulation results of this newly proposed algorithm compared to other CMA techniques such as conventional CMA, Normalized CMA (N-CMA) and Modified CMA (M-CMA). The simulation results have demonstrated that the proposed algorithm has considerably better performance than others.