Channel Equalization Using Adaptive Lattice Algorithms

In this paper, a study of adaptive lattice algorithms as applied to channel equalization is presented. The orthogonalization properties of the lattice algorithms make them appear promising for equalizing channels which exhibit heavy amplitude distortion. Furthermore, unlike the majority of other orthogonalization algorithms, the number of operations per update for the adaptive lattice equalizers is linear with respect to the number of equalizer taps.     

A Least Squares Approach to Escalator Algorithms for Adaptive Filtering

In this paper, we introduce an escalator (ESC) algorithm based on the least squares (LS) criterion. The proposed algorithm is relatively insensitive to the eigenvalue spread ratio (ESR) of an input signal and has a faster convergence speed than the conventional ESC algorithms. This algorithm exploits the fast adaptation ability of least squares methods and the orthogonalization property of the ESC structure. From the simulation results, the proposed algorithm shows superior convergence performance.     

自适应均衡算法在信道均衡技术中的应用研究

文中描述了两种非线性均衡器分别为判决反馈均衡器(DFE)和最大似然序列估计(MLSE)均衡器.所用信道模型为加性白高斯噪声信道,在DFE和线性均衡器(LE)中都是使用递归最小二乘(RLS)算法和最小均方(LMS)算法对数据进行分块处理.MLSE均衡器中使用了维特比最佳译码算法.就误比特性能来做以比较,DFE远好于LE,MLSE均衡器又明显优于DFE,并且它能达到几乎最优的性能.     

Self-Orthogonalizing Adaptive Equalization Algorithms

A comparison is made of several self-orthogonalizing adjustment algorithms for linear tapped delay line equalizers. These adaptive algorithms accelerate the rate of convergence of the equalizer tap weights to those which minimize the output mean-squared error of a data transmission system. Accelerated convergence of the estimated gradient algorithm is effected by premultiplying the correction term in the algorithm by a matrix which is an estimate of the inverse of the channel correlation matrix. The various algorithms differ in the manner in which this estimate is sequentially computed. Depending on the degree of complexity available, the equalizer convergence time may be reduced more than an order of magnitude from that required by the simple gradient algorithm.     

递归最小二乘格型滤波联合估计过程性能分析

联合估计过程有2种模式,分为总体误差更新及阶误差递归更新方式。经过格型结构滤波后,提取其中产生的后向预测误差序列作为联合估计的输入信号。通过对系统辨识问题的仿真分析,得出以下结论：如果不需要严格关注滤波模型本身噪声所带来的测量误差,从实用性的角度考虑,在总体误差更新模型中采用递归最小二乘格型联合估计处理方式（RLSLJE）性能较好,其收敛效果较为理想。     

Adaptive Least Squares Interpolation of Infrared Images

This paper presents an adaptive block-by-block least squares (LS) algorithm for the interpolation of infrared (IR) images. The suggested algorithm is based on the segmentation of the low resolution (LR) image into overlapping blocks and the interpolation of each block, separately. The purpose of the overlapping is to avoid edge effects between blocks. An iterative implementation of the proposed algorithm, which considers the image acquisition model, is used for the minimization of the estimation error in each block. A weight matrix of moderate dimensions is estimated in a small number of iterations to interpolate each block. This proposed algorithm avoids the large computational complexity resulting from the matrices of large dimensions required to interpolate the image as a whole. The performance of the proposed algorithm is compared with the standard as well as the warped distance optimal interpolation of maximal order with minimal support (O-MOMS) algorithm from the peak signal-to-noise ratio (PSNR) point of view. Numerical results reveal the superiority of the proposed LS algorithm to the cubic O-MOMS algorithm.     

基于最小二乘的时差定位算法

时差测量方程是非线性双曲线方程,可以通过引入中间变量将其转化为线性方程,对近年来国内外学者关于这种方法的时差定位算法进行了总结。当已知测量误差的先验信息时,可以采用两步加权最小二乘法和约束加权最小二乘法,当测量误差的先验信息未知时,还可以采用约束总体最小二乘的方法。在求解约束最小二乘问题时,采用常规的拉格朗日法计算复杂、运算量大,而采用高斯一牛顿法不仅可以大为降低运算量,还能提高解的精度和稳定性。此外,对约束加权最小二乘法和约束总体最小二乘法之间的关系进行了探讨,得到了它们等价性的条件。     

Adaptive Spread-Spectrum Systems Using Least-Squares Lattice Algorithms

Practical communication systems must cope with many uncertainties in addition to determining the transmitted data, e.g., the direction, timing, and distortion of the desired signal, and the spectral and spatial distribution of the interference, all of which may change with time. This paper describes exact least-squares (LS) recursive lattice algorithms which resolve these uncertainties in a direct-sequence spread-spectrum digital communication system. The adaptive LS algorithm is recursive beth in order and time, and converges rapidly to the uncertain parameters. Time-discrete algorithms may be mechanized by a receiver containing integrate-and-dump circuits operating at the chip rate of the pseudonoise (PN) sequence, one in each in-phase and each quadrature channel of each sensor array element's output. Different configurations of optimal time-discrete receivers are presented and transformed into adaptive receivers by taking advantage of the spectral properties of the different kinds of LS filters. Simulation results are presented and some guide lines are given for the architecture of an adaptive direct-sequence spread-spectrum system.     

最小二乘法在有效转换位数测试中的应用

A／D转换器广泛应用于通信和控制过程中，有效转换位数（Effective Number Of Bits．ENOB）是A／D转换器一个非常重要的指标。本文较为详细地介绍了非线性最小二乘法，并就非线性最小二乘法和线性二乘法在ENOB测试中的应用进行了分析。结果表明，线性最小二乘法更适于ENOB的测试，非线性最小二乘法则适于各类谱估计。     

Application of Fast Kalman Estimation to Adaptive Equalization

Very rapid initial convergence of the equalizer tap coefficients is a requirement of many data communication systems which employ adaptive equalizers to minimize intersymbol interference. As shown in recent papers by Godard, and by Gitlin and Magee, a recursive least squares estimation algorithm, which is a special case of the Kalman estimation algorithm, is applicable to the estimation of the optimal (minimum MSE) set of tap coefficients. It was furthermore shown to yield much faster equalizer convergence than that achieved by the simple estimated gradient algorithm, especially for severely distorted channels. We show how certain "fast recursive estimation" techniques, originally introduced by Morf and Ljung, can be adapted to the equalizer adjustment problem, resulting in the same fast convergence as the conventional Kalman implementation, but with far fewer operations per iteration (proportional to the number of equalizer taps, rather than the square of the number of equalizer taps). These fast algorithms, applicable to both linear and decision feedback equalizers, exploit a certain shift-invariance property of successive equalizer contents. The rapid convergence properties of the "fast Kalman" adaptation algorithm are confirmed by simulation.     

基于TDOA移动定位中的约束加权最小二乘算法

自E-911定位需求顽布以来，移动台定位技术在国内外受到了高度的重视和深入研究。本文主要对无线定位中基于TDOA的一种约束加权最小二来算法进行了研究。该算法通过引用一个中间变量重组非线性方程组为线性方程组，然后利用加权最小二来算法对该方程组进行求解，得到移动台的第一次位置估计值，根据得到的估计位置以及他们之间的内在联系，并引入拉格朗日来数因子重新建立一个方程来对第一次估计进行修正，求解出符合条件的拉格朗日乘数因子，从而得到第二次位置估计值。仿真结果表明在一定的噪声环境中约束加权最小二来算法能够达到克拉美罗下限，取得了较好的定位效果。     

时变衰落信道下的自适应均衡技术

短波信道的突发干扰对均衡器有严重的破坏作用并能导致均衡器失效,从而影响整个通信系统。本文则针对这一现象提出了两种处理方式,使得均衡器即使在长时间的干扰后仍能迅速恢复正常工作,保证了整个通信的连续性。计算机模拟证实了这两种处理方式的有效性。     

多站测角的机动目标最小二乘自适应跟踪算法

为了避免被动跟踪中非线性带来的计算复杂化及精度的下降问题,该文首先采用最小二乘法对目标的状态进行粗估计,然后采用当前机动目标模型和自适应跟踪算法进行线性的卡尔曼滤波,以实现对目标较高精度的定位和跟踪。实验结果表明:该方法对于匀速和匀加速运动的目标都可以达到良好的跟踪效果,其误差远小于经典的singer方法;对于强机动目标,singer方法将失效,而本文方法仍能实时辨识出目标的速度和加速度,并且估计效果良好。     

基于稀疏最小二乘支持向量回归的非线性自适应波束形成

该文基于最小二乘支持向量回归(LS-SVR)模型提出一种非线性自适应波束形成算法,以提高模型失配、小样本数、复杂多干扰等情况下的自适应波束形成器的鲁棒性。推导了高维矩阵逆矩阵求解的递推快速算法,实现了回归参数的实时求解。采用奇异性准则实时寻找输入样本集的具有较小信息冗余度的子集,并在该子集上完成波束形成计算,使得LS-SVR波束形成的求解得以稀疏化,提高了学习效率,降低了计算复杂度与系统存储空间需求。对比仿真结果验证了所提算法的正确性和有效性。     

Low Bit-Rate Image Compression via Adaptive Down-Sampling and Constrained Least Squares Upconversion

Recently, many researchers started to challenge a long-standing practice of digital photography: oversampling followed by compression and pursuing more intelligent sparse sampling techniques. In this paper, we propose a practical approach of uniform down sampling in image space and yet making the sampling adaptive by spatially varying, directional low-pass prefiltering. The resulting down-sampled prefiltered image remains a conventional square sample grid, and, thus, it can be compressed and transmitted without any change to current image coding standards and systems. The decoder first decompresses the low-resolution image and then upconverts it to the original resolution in a constrained least squares restoration process, using a 2-D piecewise autoregressive model and the knowledge of directional low-pass prefiltering. The proposed compression approach of collaborative adaptive down-sampling and upconversion (CADU) outperforms JPEG 2000 in PSNR measure at low to medium bit rates and achieves superior visual quality, as well. The superior low bit-rate performance of the CADU approach seems to suggest that oversampling not only wastes hardware resources and energy, and it could be counterproductive to image quality given a tight bit budget.      

一种抗冲击噪声的对数总体最小二乘自适应滤波算法

在未知系统输入信号和输出信号均含有噪声的环境中,传统的自适应滤波算法,如最小均方(LMS)算法,会产生有偏估计。总体最小二乘(TLS)算法能够同时最小化输入信号与输出信号的噪声干扰,是解决此类问题的重要方法。然而,在许多实际应用中,干扰噪声可能具有冲击特性,这使得传统基于2阶统计量的自适应滤波算法,包括总体最小二乘算法性能严重恶化,以至于不能正常工作。为了解决这个问题,该文在总体最小二乘法的基础上,利用对数函数对其改进,提出了一种能够抗冲击干扰的对数总体最小二乘(L-TLS)算法。最后,通过计算机仿真实验验证了该新算法的有效性。     

最小二乘算法在JTIDS相对导航中的应用

介绍了JTIDS的相对导航原理。针对JTTIS相对导航原理提出了一种基于最小二乘的滤波算法，并针对用户端机分时测距带来的滞后误差，提出了相应的解决办法。经过仿真验证，该算法能满足定位要求，且算法实现简单、效率高、适应性好等特点。     

一种抗冲击噪声的对数总体最小二乘自适应滤波算法

在未知系统输入信号和输出信号均含有噪声的环境中,传统的自适应滤波算法,如最小均方(LMS)算法,会产生有偏估计.总体最小二乘(TLS)算法能够同时最小化输入信号与输出信号的噪声干扰,是解决此类问题的重要方法.然而,在许多实际应用中,干扰噪声可能具有冲击特性,这使得传统基于2阶统计量的自适应滤波算法,包括总体最小二乘算法性能严重恶化,以至于不能正常工作.为了解决这个问题,该文在总体最小二乘法的基础上,利用对数函数对其改进,提出了一种能够抗冲击干扰的对数总体最小二乘(L-TLS)算法.最后,通过计算机仿真实验验证了该新算法的有效性.     

高速话带数据传输中的自适应均衡算法

高速数据传输中信道均衡算法大致分为三类,即基于ＬＭＳ准则,ＬＳ准则和人工神经网络的自适应算法,本文研究了这三类算法,并对部分算法作了改进,以加快收敛速度,减小稳态失调,文中的算法均用２８．８Ｋ Ｍｏｄｅｍ芯片设计的仿真系统产生的数据进行了性能测试。并给出了仿真曲线及实验结果。     

Echo Cancellation of Voiceband Data Signals Using Recursive Least Squares and Stochastic Gradient Algorithms

The convergence properties of adaptive least squares (LS) and stochastic gradient (SG) algorithms are studied in the context of echo cancellation of voiceband data signals. The algorithms considered are the SG transversal, SG lattice, LS transversal (fast Kalman), and LS lattice. It is shown that for the channel estimation problem considered here, LS algorithms converge in approximately2Niterations whereNis the order of the filter. In contrast, both SG algorithms display inferior convergence properties due to their reliance upon statistical averages. Simulations are presented to verify this result, and indicate that the fast Kalman algorithm frequently displays numerical instability which can be circumvented by using the lattice structure. Finally, the equivalence between an LS algorithm and a fast converging modified SG algorithm which uses a maximum length input data sequence is shown.