Sub-array RLS adaptive algorithm

A new sub-array asynchronous recursive least square algorithm (SARLS) is proposed. The number of elements of the smallest sub-array can be less than the number of interferers. The computational load of SARLS can be reduced to o(N) while similar array performance to that of RLS is obtained. When the sub-array size is small, SARLS will converge faster than the conventional RLS     

基于遗传算法的RLS自适应算法研究

在通信系统中采用信道均衡技术是改善信道特性行之有效的方法,但研究算法的同时,往往需要通过大量的仿真实验取平均值来选取最优的参数值,本文首先利用MATLAB仿真软件对线性调制下RLS自适应算法进行仿真分析,然后引入遗传算法的寻优特性及其优点,对RLS最佳遗忘因子λ的选取进行了寻优,得出最佳遗忘因子λ的取值,提供了参数选择的一条捷径,最后通过对比最优λ与参照λ,计算RLS算法均衡已知信号的均方误差值,证明了该方法的可行性。     

RLS adaptive blind beamforming algorithm for cyclostationarysignals

A new cost function, which is a modification of the cost function of Castedo and Figugiras-Vidal (1995) for the adaptive blind beamforming of cyclostationary signals, is proposed. The proposed cost function enables the well-known recursive least-squares technique to be applied. Simulations demonstrate that the resulting algorithm has a faster convergence speed than the stochastic gradient-based algorithm of Castedo and Figugiras-Vidal     

RLS算法在基带预失真系统中的应用

传统的最小二乘(LS)类预失真算法运算量大,且不能跟踪功放的变化特性.推导了基于递归最小二乘(RLS)算法和间接学习型结构的自适应基带预失真算法,并仿真和分析了算法的性能,结果显示该算法与传统的LS类预失真算法对系统功放非线性改善的效果相当.为了进一步验证算法的有效性,设计和构建了具有预失真功能的短波发射系统.硬件测试...     

基于LMS算法与RLS算法的自适应滤波

自适应信号处理的理论和技术已经成为人们常用滤波和去噪技术。文中讲述了自适应滤波的原理以及LMS算法和RLS算法两种基本自适应算法的原理及步骤。并用MATLAB分别对两种算法进行了自适应滤波仿真和实现。     

The fast subsampled-updating fast transversal filter (FSU FTF) RLS algorithm

We present a new fast algorithm for Recursive Least-Squares(rls) adaptive filtering that uses displacement structure and subsampled updating. Thefsu ftf algorithm is based on the Fast Transversal Filter(ftf) algorithm, which exploits the shift invariance that is present in therls adaptation of afir filter. Theftf algorithm is in essence the application of a rotation matrix to a set of filters and in that respect resembles the Levinson algorithm. In the subsampled updating approach, we accumulate the rotation matrices over some time interval before applying them to the filters. It turns out that the successive rotation matrices themselves can be obtained from a Schur type algorithm which, once properly initialized, does not require inner products. The various convolutions that thus appear in the algorithm are done using the Fast Fourier Transform(fft). For relatively long filters, the computational complexity of the new algorithm is smaller than the one of the well-known lms algorithm, rendering it especially suitable for applications such as acoustic echo cancellation.     

用于自适应数字波束形成的稳健子阵异步RLS算法

提出了一种修正的递归最小二乘自适应算法--稳健子阵异步递推最小二乘算法(MSARLS)--用于自适应数字波束形成.该算法综合运用稳健估计和子阵异步递推技术.改进后的算法,不但大大减少了运算量,而且增强了算法抗突发强干扰的性能.另还给出了计算的理论分析和计算机仿真结果.     

Fast tracking and noise-immunised RLS algorithm based on Kalmanfilter

A new least-squares algorithm based on the Kalman filter is presented. The algorithm has a self-perturbing term added to the covariance matrix, which keeps the gain vector from going infinitely small. It not only has a fast tracking capability, but also is immunised against measurement noise. The effectiveness of the algorithm is confirmed through computer simulations     

Fast parameter tracking RLS algorithm with high noise immunity

A recursive least squares (RLS) based fast parameter tracking algorithm with high noise immunity is proposed. The fast parameter tracking capability of the algorithm is achieved by perturbing the covariance matrix update equation whenever the signal model parameters change. Since the perturbing terms depends on the auto- and crosscorrelations of the signal and algorithm outputs, the proposed algorithm is very robust with respect to noise. The efficiency of the algorithm has been verified by Monte-Carlo simulations.<>     

Widely linear RLS constant modulus algorithm for complex-valued noncircular signals

Based on the constant modulus criterion, a new Widely Linear （WL） blind equalizer and a novel widely linear recursive least square constant modulus algorithm are proposed to improve the blind equalization performance for complex-valued noncircular signals. The new algorithm takes advantage of the WL filtering theory by taking full use of second-order statistical information of the complex-valued noncircular signals. Therefore, the weight vector contains the complete second-order information of the real and imaginary parts to decrease the residual inter-symbol interference effectively. Theoretical analysis and simulation results show that the proposed scheme can significantly improve the equali- zation performance for complex-valued noncircular signals compared with traditional blind equalization algorithms.     

Adaptive recovery of a chirped signal using the RLS algorithm

This paper studies the performance of the recursive least squares (RLS) algorithm in the presence of a general chirped signal and additive white noise. The chirped signal, which is a moving average (MA) signal deterministically shifted in frequency at rate ψ, can be used to model a frequency shift in a received signal. General expressions for the optimum Wiener-Hopf coefficients, one-step recovery and estimation errors, noise and lag misadjustments, and the optimum adaptation constant (β_opt) are found in terms of the parameters of the stationary MA signal. The output misadjustment is shown to be composed of a noise (ξ₀Mβ/2) and lag term (κ/(β²ψ²)), and the optimum adaptation constant is proportional to the chirp rate as ψ^2/3 . The special case of a chirped first-order autoregressive (AR1) process with correlation (α) is used to illustrate the effect the bandwidth (1/α) of the chirped signal on the adaptation parameters. It is shown that unlike for the chirped tone, where the β_opt increases with the filter length (M), the adaptation constant reaches a maximum for M near the inverse of the signal correlation (1/α). Furthermore, there is an optimum filter length for tracking the chirped signal and this length is less than (1/α)     

Systolic block Householder transformation for RLS algorithm withtwo-level pipelined implementation

The authors propose a systolic block Householder transformation (SBHT) approach to implement the HT on a systolic array and also propose its application to the RLS (recursive least squares) algorithm. Since the data are fetched in a block manner, vector operations are in general required for the vectorized array. However, a modified HT algorithm permits a two-level pipelined implementation of the SBHT systolic array at both the vector and word levels. The throughput rate can be as fast as that of the Givens rotation method. The present approach makes the HT amenable for VLSI implementation as well as applicable to real-time high-throughput applications of modern signal processing. The constrained RLS problem using the SBHT RLS systolic array is also considered     

Analysis of a stabilization technique for the fixed-pointprewindowed RLS algorithm

A stable finite precision recursive least squares (RLS) algorithm is derived for the prewindowed growing memory case (forgetting factor, λ=1). The prewindowed growing memory RLS algorithm diverges under fixed-point implementation. The random walk phenomenon due to roundoff errors in the weight update causes the divergence of the algorithm. To overcome this effect, these roundoff errors are modeled such that their effect is incorporated into the algorithm. The steady-state behavior of this new algorithm is analyzed, and it is shown that the divergence phenomenon is actually eliminated, and the new algorithm converges     

基于LMS算法与RLS算法自适应滤波及仿真分析

自适应滤波器在随机信号处理当中得到了广泛应用.文讲述了LMS算法和RLS算法的基本原理,基于两种算法的推导过程较为繁琐,不便理解,本文以两种算法的主要计算环节为突破口,选取合适的迭代公式,对两种算法进行公式推导,方便读者理解.同时,本文通过原理推导、软件仿真的试验方法,设置输入信号、噪声信号,通过输出信号的图像走势对两种算法的优缺点进行对比分析,使读者对两种算法及滤波器的设计具有直观的认识,有一定的参考意义.     

HRLS: a more efficient RLS algorithm for adaptive FIR filtering

The fast convergence rate and its immunity to the eigenvalue spread of the input correlation matrix make the RLS algorithm particularly attractive. However, the computational complexity is high. We propose using a hierarchical approach to reduce the computational complexity and further increase the convergence rate. The results of simulation runs and theoretical justifications confirm our claims     

基于RLS的二次加权相关时延估计算法

针对测向定位中时延估计的问题,提出了一种基于递推最小二乘（Recursive Least Squares,RLS）算法的二次加权相关时延估计方法。该方法在二次相关算法基础上,一方面引入RLS算法,在二次相关前进行自适应滤波,提高系统抗噪能力,且具有较快的收敛速度;另一方面借鉴广义互相关的思路,引入加权函数,并且采用二次加权方式,提高时延估计的性能。仿真结果表明,在低信噪比环境下,基于RLS的二次加权相关时延估计法使谱峰更加尖锐,抑制了噪声的影响,提高了估计的精度。     

RLS算法及其改进形式在信号分离中的应用分析

本文着重研究了自适应滤波器的重要实现形式——递推最小二乘算法（RLS）的原理,分析了RLS算法在应用中的优点及存在问题。为解决RLS算法收敛速度和稳态误差的矛盾及系统在趋于平稳时跟踪效果差的问题,本文从实现可变遗忘因子和增加自扰动项两个方面介绍了RLS算法的几种改进方法。并将它们应用于复杂电磁环境、强干扰背景下的信号分离中去。通过仿真实验,对RLS算法及其两种改进方法在信号分离中的效果进行了比较,得出可变遗忘因子RLS算法在收敛速度和分离信号的准确性上都具有较好的性能。     

An algorithm for interconnection of RTLS and RLS during object images obtaining

It is proposed an algorithm for interconnection of on-board RLS and RTLS operation in case of objects images observation, allowing to increase images resolution and improve of images recognition.     

A research of UWB rake receiver based on novel RLS adaptive algorithm

A modified RAKE receiver based on novel Recursive Least Squares （RLS） adaptive algorithm is proposed. The receiver uses L-fingered correlators, which are composed of RLS adaptive filters, to enhance the performance of multipath receiving. It can also track the amplitude of the received signal to form a real-time amplitude estimation which is correlated with the power of excess delay bin. The simulation results based on the IEEE UltraWide Band （UWB） channel models （CMI to CM4） show that the novel RLS algorithm can alter the attenuation estimation with the finger＇s power delay profile, and RAKE receiver with few fingers can be employed to get high performance.     

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。