基于MC FQRD RLS算法的功放建模方法

在地面广播电视传输系统中,为了提高效率,功率放大器通常工作在非线性区.由此引入的非线性,必须进行校正.如果能够得到功率放大器的非线性模型,校正工作就能取得很好的效果.提出了一种基于多通道快速QR分解RLS算法的建模方法.传统的QRD RLS算法具有收敛速度快和数值稳定的优点.但其缺点是算法的计算量很大,对硬件资源的需求...     

一种基于RLS算法的改进自适应算法DSRLS

在简要介绍RIS算法的基础上,从信道的角度分析该算法的不足并提出了改进算法—双边RLS算法(DSRLS)。对于该算法先从理论的角度进行仿真,从而证明该改进方法的有效性,然后推导出适合于工程的递推算法,并给出递推算法的仿真结果。仿真结果表明DSRLS算法比RLS算法在收敛后具有更好的收敛性能。     

水平机扫雷达的副瓣干扰对消算法研究

采用采样矩阵逆算法(SMI)对消副瓣干扰,一般抽取部分样本对消整段数据的干扰。当雷达阵面处于旋转扫描状态时,由于角度的变化,该对消性能下降很快。该文提出了采用递归最小二乘算法(RLS)对剩余数据进行跟踪收敛的方法,SMI RLS算法收敛速度快,且逐点样本迭代,能够跟踪干扰相对于阵面角度的变化,从而保证了对消比。同时给出了降低RLS算法计算量的方法。仿真表明,间歇算法在牺牲部分对消性能的前提下,可有效降低计算量。     

机扫雷达旁瓣对消算法研究

自适应旁瓣对消是雷达抑制有源干扰的有效措施,通常采用采样矩阵求逆的方法计算权值,对于方位上机械扫描的雷达,由于对消形成的方向图零点很窄,当天线转动时干扰的方向也会变化,针对这种情况,本文分析了运用采样矩阵求逆算法(SMI)和递归最小二乘算法(RLS)进行旁瓣对消时,干扰对消比的变化情况,并得出了RLS算法具有跟踪角度变...     

基于QRM-MLD的MIMO检测算法研究

在多输入多输出( MIMO)通信系统中,MIMO检测是发送端MIMO编码的逆过程,是根据接收到的信号及接收信号质量、信道状态信息等获得发送端发送比特的概率信息过程。通信信道矩阵的QR分解( QRD)是几种MIMO信号检测方案的基本先决条件。首先针对QRD方案进行研究,详细介绍了QRM-MLD的算法实现并进行了优化。最后对复杂度进行了统计并对仿真结果进行了分析,证明此方案可以作为MIMO检测的优选方案。     

一种加权广义预测自适应递推优化控制算法

文章给出了一种时变遗忘因子递推最小二乘-递推增广最小二乘(RLS—RELS)的性能指标加权的广义预测自适应的优化控制算法,解决了模型反馈校正中控制参数与噪声干扰紧耦合情况下参数估值变慢的问题,通过在线递推估计参数,提高了系统计算速度,并且在优化控制量过程中给出一种简便收敛递推算法,避免了逆阵的计算,提高了效率和精度,增强了鲁棒性,仿真效果表明了该算法优于传统的广义预测控制算法.     

基于矩阵广义逆递推的自适应滤波算法

本文把自适应滤波算法的优化准则之一最小二乘准则:J(n)= ∑ n i=1 λ^n-i|e(i)|²写为矩阵形式,利用矩阵广义逆递推公式直接对输入信号矩阵而不是自相关矩阵进行递推更新,得到一种新的自适应滤波算法.和其它算法如LMS算法、NLMS算法、FRLS算法、TDNLMS算法、 APA算法、Leaky-LMS算法和RLS算法进行了计算机模拟仿真比较,仿真结果表明该算法有良好的收敛性能,收敛速度快于LMS算法、NLMS算法、FRLS算法、 APA算法、Leaky-LMS算法和RLS算法.     

一种变遗忘因子RLS算法的分析与仿真

自适应信息处理的算法中的RLS算法在信号处理等方面已经得到了大量的应用。首先简要介绍了RLS算法,然后通过对RLS算法中的遗传因子的研究与分析,提出了一种新的变遗忘因子算法,通过修正函数对遗传因子进行修正,实现了此算法的优势,最后对该算法做了仿真试验。试验证明,此算法收敛速度和跟踪效果远好于普通RLS算法,并且系统稳定,具有较强的应用价值。     

一种基于无偏协方差估计RLS的自适应阵列天线抗干扰算法

RLS是自适应阵列天线抗干扰的主要算法之一。为提高RLS算法对遗忘因子选择健壮性,避免因遗忘因子选择不当所造成的算法不收敛问题,本文针对自适应阵列天线的多路接收信号,基于其无偏协方差矩阵模型,推导设计出了一种新的RLS算法,相比于常规RLS,在该算法中遗忘因子可以更加精确地控制RLS迭代过程项,降低因遗忘因子设置不当而造成的算法不收敛风险。通过仿真验证了算法的有效性。     

基于小波变换的RLS波束成形算法研究

为了解决递归最小二乘算法(RLS)在较低信噪比(SNR)、遗忘因子较小的环境中,对噪声敏感、收敛时参数估计误差大的问题,引入小波变换去噪思想,提出了基于小波变换的RLS波束成形算法。该算法利用小波变换软阈值法进行信号去噪,再采用RLS算法进行波束成形。最后对实验进行仿真,仿真结果表明,与传统的RLS算法相比,该算法具有较小的稳态误差和较快的跟踪速度和收敛速度,并且波束成形效果好。     

Fast time-series adaptive-filtering algorithm based on the QRDinverse-updates method

A new adaptive filtering algorithm for time-series data based on the QRD inverse updates method of Pan and Plemmons (1989) is derived from first principles. In common with other fast algorithms for time-series adaptive filtering, this algorithm only requires O(p) operations for the solution of a pth-order problem. Unlike previous fast algorithms based on the QRD technique, the algorithm presented here explicitly produces the transversal filter weights. Furthermore the derivation of the algorithm is achieved, quite simply, by means of signal-flow-graph manipulation. The relationship between this fast QRD inverse updates algorithm and the FTF algorithm is briefly discussed. The results of some preliminary computer simulations of the algorithm, using finite-precision floating-point arithmetic, are presented     

一种基于无平方根QR分解的流水RLS算法

传统ＱＲ分解（ＧＩＶＥＮＳ旋转）中存在着平方根运算,在用数字电路实现时,会在时间和面积上花费较大的代价,因此,已有无平方根的ＱＲ分解算法出现,这些算法避免了平方根运算,但遇射为Ｓｙｓｔｏｌｉｃ阵列以实现ＲＬＳ算法时,却不能实现单元内部的流水,本文提出了一种新的无平方要ＱＲ分解,能够实现单元内部的流水,进一步提高了速度。     

New fast QR decomposition least squares adaptive algorithms

This paper presents two new, closely related adaptive algorithms for LS system identification. The starting point for the derivation of the algorithms is the inverse Cholesky factor of the data correlation matrix, obtained via a QR decomposition (QRD). Both algorithms are of O(p) computational complexity, with p being the order of the system. The first algorithm is a fixed order QRD scheme with enhanced parallelism. The second is an order recursive lattice type algorithm based exclusively on orthogonal Givens rotations, with lower complexity compared to previously derived ones. Both algorithms are derived following a new approach, which exploits efficient the and order updates of a specific state vector quantity     

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.     

A 2-D recursive inverse adaptive algorithm

In this paper, a 2-D form of the recently proposed recursive inverse (RI) adaptive algorithm is introduced. The filter coefficients can be updated along both the horizontal and vertical directions on a 2-D plane. The proposed approach uses a variable step size and avoids the use of the inverse autocorrelation matrix in the coefficient update equation, which leads to an improved and more stable performance. Performance of the 2-D RI algorithm is compared to that of the 2-D RLS algorithm in an image deconvolution and an adaptive line enhancer problem settings. The simulation results show that the proposed 2-D RI algorithm leads to an improved performance compared to that of the 2-D RLS algorithm.     

一种基于Householder变换的递归 QRD-LS算法

本文提出了一种采用Householder变换实现的递归QRD-LS算法,该算法通过采用Householder变换取代Giv- ens旋转递归实现复矩阵的QR分解来求解LS问题,可以获得比基于Givens旋转的QRD—LS算法更快的处理速度。此外, 算法引入了复数QR分解,解决了算法只能处理实数信号的问题。通过定义新的数据矩阵,算法还可以合并求解数据域正规方程中的系数矩阵和右侧向量,从而提高了计算效率。通过对其在智能天线中的应用进行仿真,验证了算法的性能。     

Adaptive linearly constrained inverse QRD-RLS beamforming algorithm for moving jammers suppression

A general, linearly constrained (LC) recursive least squares (RLS) array-beamforming algorithm, based on an inverse QR decomposition, is developed for suppressing moving jammers efficiently. In fact, by using the inverse QR decomposition-recursive least squares (QRD-RLS) algorithm approach, the least-squares (LS) weight vector can be computed without back substitution and is suitable for implementation using a systolic array to achieve fast convergence and good numerical properties. The merits of this new constrained algorithm are verified by evaluating the performance, in terms of the learning curve, to investigate the convergence property and numerical efficiency, and the output signal-to-interference-and-noise ratio. We show that our proposed algorithm outperforms the conventional linearly constrained LMS (LCLMS) algorithm, and the one using the fast linear constrained RLS algorithm and its modified version.     

On the duality between the fast transversal and the fast QRDadaptive least squares algorithms

The duality between the fast transversal and the fast QRD adaptive least squares algorithms is established. It is shown that these two algorithmic families are related via a time-varying state transformation. The state transformation method is then applied to both the fast Kalman as well as the FAEST (PTF) algorithm to derive fast QRD counterparts     

基于SINR判决的MIMO联合检测算法

一种低复杂度的MMSE-QRD-QRDM检测方法被提出,该算法根据接收端各子流的信干噪比(SINR)和门限值的比较,对各子流分别进行最小均方误差QR分解(MMSE-QRD)检测或QR分解-M(QRD-M)检测。对MMSE-QRD-QRDM算法进行了误码率(BER)性能仿真和算法复杂度分析。结果表明,和OSD-MLD算法相比,MMSE-QRD-QRDM算法可以在大大降低检测算法复杂度的情况下,获得优于OSD-MLD算法的BER性能,同时通过减少运算中的矩阵求逆次数,提高了算法的数值稳定性。